From 00c2fe1f75e3da78ee9086e994b27d5b5b3cc5bf Mon Sep 17 00:00:00 2001 From: Fanis Vlachos <124600483+fvlachos-scottlogic@users.noreply.github.com> Date: Thu, 14 Dec 2023 11:41:32 +0000 Subject: [PATCH 01/16] Added author setup for Fanis Vlachos --- _data/authors.yml | 6 ++++++ fvlachos/atom.xml | 5 +++++ fvlachos/feed.xml | 5 +++++ fvlachos/index.html | 6 ++++++ fvlachos/picture.jpg | Bin 0 -> 994803 bytes 5 files changed, 22 insertions(+) create mode 100644 fvlachos/atom.xml create mode 100644 fvlachos/feed.xml create mode 100644 fvlachos/index.html create mode 100644 fvlachos/picture.jpg diff --git a/_data/authors.yml b/_data/authors.yml index 5b45cc16d0..f6144c212f 100644 --- a/_data/authors.yml +++ b/_data/authors.yml @@ -37,6 +37,7 @@ active-authors: - ebrown - emason - fhubin + - fvlachos - gsmith - garora - gkendall @@ -1364,3 +1365,8 @@ authors: email: sgladstone@scottlogic.com author-summary: "I'm a developer based out of our Bristol office. I'm a bit of a generalist; I like to get stuck into a range of problems from the business side to anywhere across the tech stack." picture: Image.png + fvlachos: + name: "Fanis Vlachos" + email: fvlachos@scottlogic.com + author-summary: "I am a Senior Developer at Scott Logic, based in Edinburgh. I love solving problems using software and I am passionate about learning new technologies." + picture: picture.jpg diff --git a/fvlachos/atom.xml b/fvlachos/atom.xml new file mode 100644 index 0000000000..715a91d5ca --- /dev/null +++ b/fvlachos/atom.xml @@ -0,0 +1,5 @@ +--- +author: fvlachos +layout: atom_feed +--- + diff --git a/fvlachos/feed.xml b/fvlachos/feed.xml new file mode 100644 index 0000000000..9a5f58e3b4 --- /dev/null +++ b/fvlachos/feed.xml @@ -0,0 +1,5 @@ +--- +author: fvlachos +layout: rss_feed +--- + diff --git a/fvlachos/index.html b/fvlachos/index.html new file mode 100644 index 0000000000..7085a2ab89 --- /dev/null +++ b/fvlachos/index.html @@ -0,0 +1,6 @@ +--- +title: Fanis Vlachos +author: fvlachos +layout: default_author +--- + diff --git a/fvlachos/picture.jpg b/fvlachos/picture.jpg new file mode 100644 index 0000000000000000000000000000000000000000..d504bff99ee312601b3efd316136efed25610e7f GIT binary patch literal 994803 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zv58=sTxHKZ{{X;$#)>J{ZFO-#KW@nj3E`z!`oj*aeYll*V1qyh;$s`QdOX1rXV7x;bY&lVtSNMNv94>Z`Z`V2c^IqSj#IUx|Hx1bNx%B@48Yr)m!|czlt??H11q%*( z=hyuER|(>QzixYge=$WB(=!iJlH?@E8}mmBJP(mt~fpzP_z?K?aH|zcowPGswy0{a!>eIjB9g9(Sg0bZMpnM`TXdjz1*MUXOo(L2*i0eAZ?%( zk0Tl5k^X-QyKdX9SoGS*7(VCxD5AZ5%Vc@kH`**(x#W7Xslgze9CfawOQ)dF~Wgg19@HsIuB|nqP(7`pnw0_>IlG# literal 0 HcmV?d00001 From 7eee4343d5735640c37d63a2de3b1f9ffe3695d1 Mon Sep 17 00:00:00 2001 From: Fanis Vlachos <124600483+fvlachos-scottlogic@users.noreply.github.com> Date: Thu, 14 Dec 2023 12:11:48 +0000 Subject: [PATCH 02/16] Added blog on implemented cost-effective TDD in an LLM aplication --- ...development-in-an-llm-application.markdown | 66 +++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 _posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown diff --git a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown new file mode 100644 index 0000000000..82df4949e1 --- /dev/null +++ b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown @@ -0,0 +1,66 @@ +--- +title: 'Implementing cost-effective test-driven development in an LLM application' +date: 2023-12-15 14:00:00 Z +categories: +- fvlachos +- Artificial Intelligence +tags: +- Testing +- LLM +- Artificial Intelligence +- LangChain +- Blog +- GPT +- Sustainability +summary: Teething troubles with our nascent chatbot +author: fvlachos +image: "/uploads/Scottbot%20tn.png" +--- + +## **Introduction** +In our continuous pursuit of refining [Scottbot, our LLM-powered chatbot]({{ site.github.url }}/2023/10/23/building_scottbot_an_ai_chatbot_for_scott_logic.html), we have encountered and overcome various challenges. The implementation of Test-driven development practices and comprehensive testing, [a topic previously explored in a dedicated blog]({{ site.github.url }}/2023/11/14/testing-LLM-based-applications-strategy-and-challenges.html), was a particularly difficult one due to the non-deterministic nature of the LLMs. Despite conquering these initial obstacles, a new challenge emerged – one that revolved around managing the costs associated with testing. In this blog post, I will explore the intricacies of this cost challenge and outline the strategic measures we undertook to streamline our test suite, thereby reducing expenses and optimizing our testing processes. + +## **Understanding our testing approach** +Our testing framework treats the application as a black box - it sends a prompt and evaluates the final response without considering how that response was generated. Given the non-deterministic nature of the underlying Language Learning Model (LLM), relying on string comparisons to judge the response's quality would be futile. Instead, string comparison is reserved solely for verifying the presence of URLs in the response, which indicate the bot's source of information. To accurately assess the quality of the bot's output, we employ a secondary LLM that evaluates the response against a set of reference information we provide. Moreover, recognizing the inherent variability in the LLM's outputs, we do not rely on single-instance testing. Multiple runs are conducted for each prompt, and the results are assessed on an average basis to account for the LLM's non-deterministic behaviour, thus providing a more robust measure of performance. [You can read more on how statistics can be utilised to address randomness here]({{ site.github.url }}/2023/11/16/using-the-t-test-for-effective-testing-of-nondeterministic-AI-systems.html). + +## **New challenges** +It should be no surprise that our solution to the non-deterministic nature of our application lead to a new challenge – a significant increase in operational costs. While our project budget was not immediately threatened and we expected that LLM costs would decrease over time, we recognized the importance of resource efficiency, especially since this project served as a training ground for our team. The financial aspect, although important, was not the only consideration; we were equally conscious of our environmental footprint, acknowledging that every computation has a tangible impact on our planet. Moreover, the introduction of the evaluation LLM and the multiple tests runs along with the inherent latency in LLM responses, increased the duration of our test cycle substantially. This had a knock-on effect on our development cycles, slowing down the pace at which we could deliver new features and improvements. Therefore, we recognised that optimizing our test suite was essential. + +## **Caching** +Caching was naturally the first solution we explored. The Langchain library, which is a cornerstone of our project, offers a caching mechanism for storing responses to prompts that are frequently used. We started with caching the bot’s initialization process which involves an LLM call – specifically sending an initial message along with the system prompt. To prevent any interference between our tests, it was crucial to clear the bot's memory and re-initialize it after each test. Therefore, caching this call made a notable improvement. + +Our testing methodology often involved sending the same prompts multiple times to thoroughly evaluate different facets of Scottbot's capabilities, including response accuracy, tone consistency, and the provision of URL sources. By splitting these prompts into separate tests, we gained a granular insight into the bot’s performance. Caching was instrumental in this, as it allowed us to repurpose the LLM's responses across various tests, significantly streamlining the process. In conclusion, caching resulting in cutting down on the number of LLM requests and therefore reducing the cost and duration of our test suite. + +## **Rethinking our testing strategy** +Despite the benefits of caching, we quickly realized that it was not the silver bullet for our cost issues. The crux of the issue lay in the numerous calls to the LLM required to generate just a single response. For every user query the application uses: +* An LLM-powered agent to decide which tool to use to retrieve the necessary information. +* An LLM-powered tool (or multiple). +* An LLM to combine the information returned by the tool along with the conversation context to generate the final response. + +This led to us re-evaluating our testing strategy – perhaps black box testing was not the optimal approach as it makes it impossible to avoid any of the aforementioned LLM calls. Consequently, we began exploring how we could break down our monolithic test suite into smaller, more focused tests that target specific components of the pipeline. The advantages of this approach were immediately apparent. First, it would be cheaper to run since we could now target specific parts of the application rather than the whole. Second, it would pinpoint exactly which component was failing, thereby simplifying the debugging process. The subsequent sections will delve into how this was implemented. + +## **Agent tests** +The first component to isolate is the agent’s decision-making process. It is easy to understand if the agent selects the incorrect tool, the bot’s final response will be flawed. Therefore, there is no need to carry out the rest of the LLM calls or evaluate the final response. Based on this, we designed a test suite to assess the agent’s tool invocation abilities. This suite evaluates which tool the agent chooses and the input it uses it with, in response to a given prompt. As each test need just a single LLM call, they are significantly cheaper and faster. + +These tests became particularly useful when we implemented changes that could affect the agent’s decision-making process. For example, the agent type, the system prompt, the tools descriptions, or the agent’s underlying LLM. + +## **Vector store retrieval tests** +The next component to isolate was the vector store retrieval process. All internal documents that the bot needs to access to reply to Scott Logic-related questions have been converted into embeddings and been stored in a vector database. During runtime, Scottbot conducts a similarity search based on the user's query to retrieve the most relevant document(s). These documents, along with the user's question, are then passed to the LLM, which attempts to generate an accurate answer. + +The accuracy of the bot’s response is heavily dependent on the vector store’s ability to return the correct documents. If the retrieval process is flawed, the responses will inevitably be inaccurate. Therefore, we developed a dedicated test suite focusing on whether the vector store retrieves the correct documents for a given query. + +This suite was particularly helpful when changes were made that could impact the retrieval process. For example, modifications to the similarity search algorithm, its configuration, the document-to-embedding conversion process, or the document segmentation to accommodate the LLM's context. + +One of the key advantages of isolating the vector store retrieval process is that it does not rely on any LLM calls – this process is completely deterministic. This results in tests that are not only cost-effective but also rapid, providing us with the ability to swiftly identify and rectify any discrepancies in the retrieval process. + +## **Retrieval QA Chain tests** +Finally, we isolated the process that last part of the Scott Logic tool – the invocation of the Retrieval QA Chain. This process involves providing the LLM with the context fetched from our vector store and the user query and asking it to generate an answer. + +Assuming the provided context is adequately comprehensive, these tests can provide various insights. Through these tests, we were able to adjust the configuration settings of the Retrieval QA Chain. This included determining the ideal number of context documents to utilize from the vector store and the most effective method of presenting them to the LLM — be it through appending the documents or generating a synthesized summary. Moreover, the tests facilitated a comparative analysis of various LLMs, enabling us to identify the most suitable model for the question-answering part of our application. + +Finally, these tests provided an easier way to ensure the source is consistently provided. This is because the response of the retrieval QA chain is a dictionary with an answer and a source key, instead of a natural language response. Therefore, we could easily check with simple code whether it is empty instead of relying on a secondary LLM to evaluate the final response of the bot. + +## **Conclusion** +Throughout the development of Scottbot, we have faced the complex challenges inherent in testing AI-powered applications. The non-deterministic nature of LLMs, coupled with the cost incurred with each usage, means that conventional, end-to-end black box testing is not cost-effective. + +Nevertheless, there are always strategies to enhance testing efficiency and manage resources more effectively. For us, implementing a caching system for commonly used LLM responses and breaking down our monolithic test suite into focused, component-specific suites, reduced our operational costs and opened new ways for improving the application’s performance. A key takeaway from this project is that the diligent design of a cost-effective test suite from the beginning important and depending on the budget, might be necessary. 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b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown index 82df4949e1..323501053d 100644 --- a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown +++ b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown @@ -1,6 +1,6 @@ --- title: 'Implementing cost-effective test-driven development in an LLM application' -date: 2023-12-15 14:00:00 Z +date: 2023-12-13 14:00:00 Z categories: - fvlachos - Artificial Intelligence From 607b48a1e938d4e0c928318b4d2b8036dd8b1e0a Mon Sep 17 00:00:00 2001 From: Fanis Vlachos <124600483+fvlachos-scottlogic@users.noreply.github.com> Date: Thu, 14 Dec 2023 12:51:01 +0000 Subject: [PATCH 05/16] Fixing bullet point formatting --- ...ective-test-driven-development-in-an-llm-application.markdown | 1 + 1 file changed, 1 insertion(+) diff --git a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown index 323501053d..3c1a8b89c5 100644 --- a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown +++ b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown @@ -33,6 +33,7 @@ Our testing methodology often involved sending the same prompts multiple times t ## **Rethinking our testing strategy** Despite the benefits of caching, we quickly realized that it was not the silver bullet for our cost issues. The crux of the issue lay in the numerous calls to the LLM required to generate just a single response. For every user query the application uses: + * An LLM-powered agent to decide which tool to use to retrieve the necessary information. * An LLM-powered tool (or multiple). * An LLM to combine the information returned by the tool along with the conversation context to generate the final response. 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zDL-1ZXx)(|udSzF*yz)fOXSTxEWatF(0NesJXt6q<>cNx1RsBa9H#x3RoK9GITt0( zt(yRj^~=${OCv<&^5)1D7L+T>2X1!vY1j3%>aC-p%hmNW%R2TNldo~I9oJ=<=b3P} z=tfnPv{I!*PIO}L$<WKQuKj~H!TsN1*59$sO^3SGzd6m+Up;8ml<~j2N@i5@Z;tGw zs}}nGhpg-SSoWCrUk~>k@AR1@Ot5E4SYK8+(vww#WxqFa?EEq_0l=?!uP?@*XF<Ll zUsLfn=f7E4F^^%ZVo&WN6z9+hohFLkV%)LorPX4e>`PRmufd*t`&+Tyj`Yu*XzU9C zI>o1W^W^mK3F40b0E*1@J*GPKxb=0~5}Gk|qjvo4`d6~Y0jJct%N2#^M0+`~uc#;< zJ!fP4Mz-CfS7fy8hf|(ChD^D${C32;amz-?ZOvLWIa%k00be=MJWth!QGWjbA8^0h zW0Jr2ENKl>eK5t<rmK4NT1Uv$4QPYNt9hPz$A*1*%b&REv&SRi&yt-Mn=IRAy&Z;J zQ}Opx#Kl;JKEQcJ;ZN2c>9nqVeMP6P_Uk(>k&<?Coi*o{-nabMr2VZW_{yN4x{nYC zJtir`%e049ow9GWR=Blkr59FxP=ykHOy+E7KTE$RsCghCr`DZ?hNPUb*Hz7QX4%%) zOAK0*H&V%?JrR;~qP<vtC-2DM2BzM!c`}*nUdmZ8uFVx{rKJTM9j7Q3qi2brQdpK^ HNc#WT1ufWe From 1706776b972ddd6f328fbaadaf01a964cde3667a Mon Sep 17 00:00:00 2001 From: Fanis Vlachos <124600483+fvlachos-scottlogic@users.noreply.github.com> Date: Thu, 14 Dec 2023 14:14:23 +0000 Subject: [PATCH 07/16] Added summary --- ...-test-driven-development-in-an-llm-application.markdown | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown index 3c1a8b89c5..34951e9fcc 100644 --- a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown +++ b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown @@ -1,5 +1,5 @@ --- -title: 'Implementing cost-effective test-driven development in an LLM application' +title: 'Implementing cost-effective Test-Driven Development in an LLM application' date: 2023-12-13 14:00:00 Z categories: - fvlachos @@ -12,13 +12,12 @@ tags: - Blog - GPT - Sustainability -summary: Teething troubles with our nascent chatbot +summary: Developing LLM applications using Test-Driven Development principles can be expensive due to the incurred cost of every usage. In this post, we discussed how our attempt to tackle the inherit non-deterministic nature of LLMs increased our operational costs and the techniques we employed to minimize them. author: fvlachos -image: "/uploads/Scottbot%20tn.png" --- ## **Introduction** -In our continuous pursuit of refining [Scottbot, our LLM-powered chatbot]({{ site.github.url }}/2023/10/23/building_scottbot_an_ai_chatbot_for_scott_logic.html), we have encountered and overcome various challenges. The implementation of Test-driven development practices and comprehensive testing, [a topic previously explored in a dedicated blog]({{ site.github.url }}/2023/11/14/testing-LLM-based-applications-strategy-and-challenges.html), was a particularly difficult one due to the non-deterministic nature of the LLMs. Despite conquering these initial obstacles, a new challenge emerged – one that revolved around managing the costs associated with testing. In this blog post, I will explore the intricacies of this cost challenge and outline the strategic measures we undertook to streamline our test suite, thereby reducing expenses and optimizing our testing processes. +In our continuous pursuit of refining [Scottbot, our LLM-powered chatbot]({{ site.github.url }}/2023/10/23/building_scottbot_an_ai_chatbot_for_scott_logic.html), we have encountered and overcome various challenges. The implementation of Test-Driven Development practices and comprehensive testing, [a topic previously explored in a dedicated blog]({{ site.github.url }}/2023/11/14/testing-LLM-based-applications-strategy-and-challenges.html), was a particularly difficult one due to the non-deterministic nature of the LLMs. Despite conquering these initial obstacles, a new challenge emerged – one that revolved around managing the costs associated with testing. In this blog post, I will explore the intricacies of this cost challenge and outline the strategic measures we undertook to streamline our test suite, thereby reducing expenses and optimizing our testing processes. ## **Understanding our testing approach** Our testing framework treats the application as a black box - it sends a prompt and evaluates the final response without considering how that response was generated. Given the non-deterministic nature of the underlying Language Learning Model (LLM), relying on string comparisons to judge the response's quality would be futile. Instead, string comparison is reserved solely for verifying the presence of URLs in the response, which indicate the bot's source of information. To accurately assess the quality of the bot's output, we employ a secondary LLM that evaluates the response against a set of reference information we provide. Moreover, recognizing the inherent variability in the LLM's outputs, we do not rely on single-instance testing. Multiple runs are conducted for each prompt, and the results are assessed on an average basis to account for the LLM's non-deterministic behaviour, thus providing a more robust measure of performance. [You can read more on how statistics can be utilised to address randomness here]({{ site.github.url }}/2023/11/16/using-the-t-test-for-effective-testing-of-nondeterministic-AI-systems.html). From 8e5f8d2de00be55d13d5b659178309b7b01dcb8e Mon Sep 17 00:00:00 2001 From: Fanis Vlachos <124600483+fvlachos-scottlogic@users.noreply.github.com> Date: Thu, 14 Dec 2023 15:26:42 +0000 Subject: [PATCH 08/16] Added diagram for agent decision making --- ...n-development-in-an-llm-application.markdown | 11 ++++++++--- _uploads/scottbot_agent_decision_making.png | Bin 0 -> 51629 bytes 2 files changed, 8 insertions(+), 3 deletions(-) create mode 100644 _uploads/scottbot_agent_decision_making.png diff --git a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown index 34951e9fcc..d72fa7b001 100644 --- a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown +++ b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown @@ -12,7 +12,7 @@ tags: - Blog - GPT - Sustainability -summary: Developing LLM applications using Test-Driven Development principles can be expensive due to the incurred cost of every usage. In this post, we discussed how our attempt to tackle the inherit non-deterministic nature of LLMs increased our operational costs and the techniques we employed to minimize them. +summary: Developing LLM applications using Test-Driven Development principles can be expensive due to the incurred cost of every usage. In this post, we discussed how our attempt to tackle the inherit non-deterministic nature of LLMs increased our operational costs and the techniques we employed to minimise them. author: fvlachos --- @@ -39,8 +39,13 @@ Despite the benefits of caching, we quickly realized that it was not the silver This led to us re-evaluating our testing strategy – perhaps black box testing was not the optimal approach as it makes it impossible to avoid any of the aforementioned LLM calls. Consequently, we began exploring how we could break down our monolithic test suite into smaller, more focused tests that target specific components of the pipeline. The advantages of this approach were immediately apparent. First, it would be cheaper to run since we could now target specific parts of the application rather than the whole. Second, it would pinpoint exactly which component was failing, thereby simplifying the debugging process. The subsequent sections will delve into how this was implemented. -## **Agent tests** -The first component to isolate is the agent’s decision-making process. It is easy to understand if the agent selects the incorrect tool, the bot’s final response will be flawed. Therefore, there is no need to carry out the rest of the LLM calls or evaluate the final response. Based on this, we designed a test suite to assess the agent’s tool invocation abilities. This suite evaluates which tool the agent chooses and the input it uses it with, in response to a given prompt. As each test need just a single LLM call, they are significantly cheaper and faster. +## **Agent decision-making tests** +The first component to isolate is the agent’s decision-making process, which is illustrated in the diagram below. + + +![scottbot_agent_decision_making.png]({{ site.github.url }}/uploads/scottbot_agent_decision_making.png) + +It is easy to understand if the agent selects the incorrect tool, the bot’s final response will be flawed. Therefore, there is no need to carry out the rest of the LLM calls or evaluate the final response. Based on this, we designed a test suite to assess the agent’s tool invocation abilities. This suite evaluates which tool the agent chooses and the input it uses it with, in response to a given prompt. As each test need just a single LLM call, they are significantly cheaper and faster. These tests became particularly useful when we implemented changes that could affect the agent’s decision-making process. 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z2R|JAd!QI-m;HJ=o)9|u<EMY)B!5oB4iwkCqMSv!KO6m>TJ+~M`gu(;S1oi#!v5$9 z`o9Kp<#f`WKYId|Tnkk4mYhoJKPT~bGV;&4>OmOBHd4o(2S*tE<o+VqpJe<~srZXv zf1R*DF4EsDmA|`^g>|UBl^cJ5a|r_J0)G+4&kPqnxs#(q0RLg+)#dVKO>h4X8Bp_e literal 0 HcmV?d00001 From 3932f9703c5c8ca3d4a2d8b5b9cc01ad5acb531c Mon Sep 17 00:00:00 2001 From: Fanis Vlachos <124600483+fvlachos-scottlogic@users.noreply.github.com> Date: Thu, 14 Dec 2023 16:28:46 +0000 Subject: [PATCH 09/16] Added diagrams for scott logic database tool --- ...development-in-an-llm-application.markdown | 11 +++++++---- _uploads/Vector_store_retrieval_process.png | Bin 0 -> 66758 bytes _uploads/retrieval_qa_chain.png | Bin 0 -> 67290 bytes _uploads/scottbot_agent_decision_making.png | Bin 51629 -> 66625 bytes 4 files changed, 7 insertions(+), 4 deletions(-) create mode 100644 _uploads/Vector_store_retrieval_process.png create mode 100644 _uploads/retrieval_qa_chain.png diff --git a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown index d72fa7b001..dcf2da92a6 100644 --- a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown +++ b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown @@ -42,15 +42,16 @@ This led to us re-evaluating our testing strategy – perhaps black box testing ## **Agent decision-making tests** The first component to isolate is the agent’s decision-making process, which is illustrated in the diagram below. - ![scottbot_agent_decision_making.png]({{ site.github.url }}/uploads/scottbot_agent_decision_making.png) -It is easy to understand if the agent selects the incorrect tool, the bot’s final response will be flawed. Therefore, there is no need to carry out the rest of the LLM calls or evaluate the final response. Based on this, we designed a test suite to assess the agent’s tool invocation abilities. This suite evaluates which tool the agent chooses and the input it uses it with, in response to a given prompt. As each test need just a single LLM call, they are significantly cheaper and faster. +It is easy to understand if the agent selects the incorrect tool, the bot’s final response will be flawed. Therefore, there is no need to actually invoke the tool or evaluate the final response. Based on this, we designed a test suite to assess the agent’s tool invocation abilities. This suite evaluates which tool the agent chooses and the input it uses it with, in response to a given prompt. As each test need just a single LLM call, they are significantly cheaper and faster. These tests became particularly useful when we implemented changes that could affect the agent’s decision-making process. For example, the agent type, the system prompt, the tools descriptions, or the agent’s underlying LLM. ## **Vector store retrieval tests** -The next component to isolate was the vector store retrieval process. All internal documents that the bot needs to access to reply to Scott Logic-related questions have been converted into embeddings and been stored in a vector database. During runtime, Scottbot conducts a similarity search based on the user's query to retrieve the most relevant document(s). These documents, along with the user's question, are then passed to the LLM, which attempts to generate an accurate answer. +The next component to isolate was the vector store retrieval process. All internal documents that the bot needs to access to reply to Scott Logic-related questions have been converted into embeddings and been stored in a vector database. During runtime, Scottbot conducts a similarity search based on the user's query to retrieve the most relevant document(s). These documents, along with the user's question, are then passed to the LLM, which attempts to generate an accurate answer. An diagram of this process can be seen below. + +![Vector_store_retrieval_process.png]({{ site.github.url }}/uploads/Vector_store_retrieval_process.png) The accuracy of the bot’s response is heavily dependent on the vector store’s ability to return the correct documents. If the retrieval process is flawed, the responses will inevitably be inaccurate. Therefore, we developed a dedicated test suite focusing on whether the vector store retrieves the correct documents for a given query. @@ -59,7 +60,9 @@ This suite was particularly helpful when changes were made that could impact the One of the key advantages of isolating the vector store retrieval process is that it does not rely on any LLM calls – this process is completely deterministic. This results in tests that are not only cost-effective but also rapid, providing us with the ability to swiftly identify and rectify any discrepancies in the retrieval process. ## **Retrieval QA Chain tests** -Finally, we isolated the process that last part of the Scott Logic tool – the invocation of the Retrieval QA Chain. This process involves providing the LLM with the context fetched from our vector store and the user query and asking it to generate an answer. +Finally, we isolated the process that last part of the Scott Logic tool – the invocation of the Retrieval QA Chain. This process involves providing the LLM with the context fetched from our vector store and the user query and asking it to generate an answer. + +![retrieval_qa_chain.png]({{ site.github.url }}/uploads/retrieval_qa_chain.png) Assuming the provided context is adequately comprehensive, these tests can provide various insights. Through these tests, we were able to adjust the configuration settings of the Retrieval QA Chain. This included determining the ideal number of context documents to utilize from the vector store and the most effective method of presenting them to the LLM — be it through appending the documents or generating a synthesized summary. 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zdj4IoTYRRN9DNCWU>IzsdiH47wCaFbVG~(;HZ<Z)p$QNIlT8F@#o(!B0W9zT1%vms zwzjVD+4}AbIHcpNvppghH)fqNPsC(Gr|5$nsXEO@NYn>B@SX0r-t0?_P%FH?FZtZ9 z8x=QKzI+dJW7hiTt*_Yo+;)T4$xqyNbaL{3USi6(x_kR0IF<?-!8>+cd6t8i#89Qa z@TTCiiQOGQ{Y1PFy45qA))2Uo8Z|XuPy#RMnSbYudn}m9gL&u0N~<<Dopncmgdz*v zOgTEjpQzYQUaT*a+*hyyhLl(X%Y=xp|GhY4IM|NBL`Px=<w-$8wyXv?l}EUY$@Tp& zi-7;y8~K-{{w=Bhb+&23fB7fHjFzdVXZahus?(rT#e<I4?sf!xvxr9?JgjVm3UJ#P zU(Bf^fBgPeQE<D*&oYBG`MVL?G!i6=*p<Gdzcu=s_WVbqfO<yg-Yz#s3cSOA{Pa)N z2R|JAd!QI-m;HJ=o)9|u<EMY)B!5oB4iwkCqMSv!KO6m>TJ+~M`gu(;S1oi#!v5$9 z`o9Kp<#f`WKYId|Tnkk4mYhoJKPT~bGV;&4>OmOBHd4o(2S*tE<o+VqpJe<~srZXv zf1R*DF4EsDmA|`^g>|UBl^cJ5a|r_J0)G+4&kPqnxs#(q0RLg+)#dVKO>h4X8Bp_e From 58e14977279c7381e1f6067ed1ca8ececa498c95 Mon Sep 17 00:00:00 2001 From: Fanis Vlachos <124600483+fvlachos-scottlogic@users.noreply.github.com> Date: Thu, 14 Dec 2023 16:50:57 +0000 Subject: [PATCH 10/16] trying to improve image readability --- ...development-in-an-llm-application.markdown | 8 +++++--- _uploads/retrieval_qa_chain.png | Bin 67290 -> 64800 bytes 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown index dcf2da92a6..fb08121f35 100644 --- a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown +++ b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown @@ -23,7 +23,9 @@ In our continuous pursuit of refining [Scottbot, our LLM-powered chatbot]({{ sit Our testing framework treats the application as a black box - it sends a prompt and evaluates the final response without considering how that response was generated. Given the non-deterministic nature of the underlying Language Learning Model (LLM), relying on string comparisons to judge the response's quality would be futile. Instead, string comparison is reserved solely for verifying the presence of URLs in the response, which indicate the bot's source of information. To accurately assess the quality of the bot's output, we employ a secondary LLM that evaluates the response against a set of reference information we provide. Moreover, recognizing the inherent variability in the LLM's outputs, we do not rely on single-instance testing. Multiple runs are conducted for each prompt, and the results are assessed on an average basis to account for the LLM's non-deterministic behaviour, thus providing a more robust measure of performance. [You can read more on how statistics can be utilised to address randomness here]({{ site.github.url }}/2023/11/16/using-the-t-test-for-effective-testing-of-nondeterministic-AI-systems.html). ## **New challenges** -It should be no surprise that our solution to the non-deterministic nature of our application lead to a new challenge – a significant increase in operational costs. While our project budget was not immediately threatened and we expected that LLM costs would decrease over time, we recognized the importance of resource efficiency, especially since this project served as a training ground for our team. The financial aspect, although important, was not the only consideration; we were equally conscious of our environmental footprint, acknowledging that every computation has a tangible impact on our planet. Moreover, the introduction of the evaluation LLM and the multiple tests runs along with the inherent latency in LLM responses, increased the duration of our test cycle substantially. This had a knock-on effect on our development cycles, slowing down the pace at which we could deliver new features and improvements. Therefore, we recognised that optimizing our test suite was essential. +It should be no surprise that our solution to the non-deterministic nature of our application lead to a new challenge – a significant increase in operational costs. While our project budget was not immediately threatened and we expected that LLM costs would decrease over time, we recognized the importance of resource efficiency, especially since this project served as a training ground for our team. + +The financial aspect, although important, was not the only consideration; we were equally conscious of our environmental footprint, acknowledging that every computation has a tangible impact on our planet. Moreover, the introduction of the evaluation LLM and the multiple tests runs along with the inherent latency in LLM responses, increased the duration of our test cycle substantially. This had a knock-on effect on our development cycles, slowing down the pace at which we could deliver new features and improvements. Therefore, we recognised that optimizing our test suite was essential. ## **Caching** Caching was naturally the first solution we explored. The Langchain library, which is a cornerstone of our project, offers a caching mechanism for storing responses to prompts that are frequently used. We started with caching the bot’s initialization process which involves an LLM call – specifically sending an initial message along with the system prompt. To prevent any interference between our tests, it was crucial to clear the bot's memory and re-initialize it after each test. Therefore, caching this call made a notable improvement. @@ -49,7 +51,7 @@ It is easy to understand if the agent selects the incorrect tool, the bot’s fi These tests became particularly useful when we implemented changes that could affect the agent’s decision-making process. For example, the agent type, the system prompt, the tools descriptions, or the agent’s underlying LLM. ## **Vector store retrieval tests** -The next component to isolate was the vector store retrieval process. All internal documents that the bot needs to access to reply to Scott Logic-related questions have been converted into embeddings and been stored in a vector database. During runtime, Scottbot conducts a similarity search based on the user's query to retrieve the most relevant document(s). These documents, along with the user's question, are then passed to the LLM, which attempts to generate an accurate answer. An diagram of this process can be seen below. +The next component to isolate was the vector store retrieval process. All internal documents that the bot needs to access to reply to Scott Logic-related questions have been converted into embeddings and been stored in a vector database. During runtime, Scottbot conducts a similarity search based on the user's query to retrieve the most relevant document(s). These documents, along with the user's question, are then passed to the LLM, which attempts to generate an accurate answer. A diagram of this process can be seen below. ![Vector_store_retrieval_process.png]({{ site.github.url }}/uploads/Vector_store_retrieval_process.png) @@ -60,7 +62,7 @@ This suite was particularly helpful when changes were made that could impact the One of the key advantages of isolating the vector store retrieval process is that it does not rely on any LLM calls – this process is completely deterministic. This results in tests that are not only cost-effective but also rapid, providing us with the ability to swiftly identify and rectify any discrepancies in the retrieval process. ## **Retrieval QA Chain tests** -Finally, we isolated the process that last part of the Scott Logic tool – the invocation of the Retrieval QA Chain. This process involves providing the LLM with the context fetched from our vector store and the user query and asking it to generate an answer. +Finally, we isolated the process of the last part of the Scott Logic tool – the invocation of the Retrieval QA Chain. 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z<33p%XkaF@c}HS2Mj7svTd&1Pv2Ete#8Cll)PdnV*;8AnX-*{5VX)v-Dec8|NJAVN z(^A{#KJ}P&7cpJtpO*X+Lq**s`0jPrns`w>aKo5kjN@!;w)Z`30B3H*?sGrhgwmp? z9ajK1pY+?{za%4rUBGdPi+V_uz{GOVrv$5T3KO>@aw=K>4x!$itq^q}SIWIrKNQzr zEHE`E%1QngW^j9$lKA+l72%zV{gMT4i=fP%lkd%`Ka?P4<4e4j{b(;X5#bDy*Y`2k zm1yO)2b1iRy|}*pH$<eia0G^bLqsHZ|C<o8%`0)}{Rx^75i(ns$#Z?-cfv2SNh$<% z1|0Cdh67leAwK=fgNHQADnX0hsKq~%{wJR1vF3sQk0<{=KqE7Wky0M3m^(I8s~TL& zFrn$-Q*o$T+)|4BF#i>#e`Jb450kI%52jHV)PiKK0~3$elLcrMB$Xyw%>t(dks~yQ zpE^P#%rWw7qO1S^%t;fUpt<-ZvKkK3Wl+wPsQ=}EKJ4JL9x4py@Wzb*4T+SMSVsaU z=!w15K{*Yv`>Y68%Kv2qJ?p=Wz!CN@BmQLstw#D65&t6Me~*a6MlB4dLjRxkQ-XAb zn+O#Fav8HLRc{xaq=PHDXx&d5YbyrUcILn#TCbOCQP%YE0bAB@Q))ke+B9crGpy*f X@>Gx(MI>&}!GC5(mWIU!&S(D*VCDga From 44f21ac258483ddd5888949b47f0e7c485b28ee6 Mon Sep 17 00:00:00 2001 From: Fanis Vlachos <124600483+fvlachos-scottlogic@users.noreply.github.com> Date: Thu, 14 Dec 2023 17:43:26 +0000 Subject: [PATCH 12/16] Improved diagrams and added some clarifications on the text --- ...development-in-an-llm-application.markdown | 6 +++--- _uploads/Vector_store_retrieval_process.png | Bin 68720 -> 68165 bytes _uploads/scottbot_agent_decision_making.png | Bin 66625 -> 110244 bytes 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown index fb08121f35..40b0fc0572 100644 --- a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown +++ b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown @@ -17,10 +17,10 @@ author: fvlachos --- ## **Introduction** -In our continuous pursuit of refining [Scottbot, our LLM-powered chatbot]({{ site.github.url }}/2023/10/23/building_scottbot_an_ai_chatbot_for_scott_logic.html), we have encountered and overcome various challenges. The implementation of Test-Driven Development practices and comprehensive testing, [a topic previously explored in a dedicated blog]({{ site.github.url }}/2023/11/14/testing-LLM-based-applications-strategy-and-challenges.html), was a particularly difficult one due to the non-deterministic nature of the LLMs. Despite conquering these initial obstacles, a new challenge emerged – one that revolved around managing the costs associated with testing. In this blog post, I will explore the intricacies of this cost challenge and outline the strategic measures we undertook to streamline our test suite, thereby reducing expenses and optimizing our testing processes. +In our continuous pursuit of refining [Scottbot, our LLM-powered chatbot]({{ site.github.url }}/2023/10/23/building_scottbot_an_ai_chatbot_for_scott_logic.html), we have encountered and overcome various challenges. The implementation of Test-Driven Development practices and comprehensive testing, [a topic previously explored in a dedicated blog]({{ site.github.url }}/2023/11/14/testing-LLM-based-applications-strategy-and-challenges.html), was a particularly difficult one due to the non-deterministic nature of the Language Learning Models (LLMs). Despite conquering these initial obstacles, a new challenge emerged – one that revolved around managing the costs associated with testing. In this blog post, I will explore the intricacies of this cost challenge and outline the strategic measures we undertook to streamline our test suite, thereby reducing expenses and optimizing our testing processes. ## **Understanding our testing approach** -Our testing framework treats the application as a black box - it sends a prompt and evaluates the final response without considering how that response was generated. Given the non-deterministic nature of the underlying Language Learning Model (LLM), relying on string comparisons to judge the response's quality would be futile. Instead, string comparison is reserved solely for verifying the presence of URLs in the response, which indicate the bot's source of information. To accurately assess the quality of the bot's output, we employ a secondary LLM that evaluates the response against a set of reference information we provide. Moreover, recognizing the inherent variability in the LLM's outputs, we do not rely on single-instance testing. Multiple runs are conducted for each prompt, and the results are assessed on an average basis to account for the LLM's non-deterministic behaviour, thus providing a more robust measure of performance. [You can read more on how statistics can be utilised to address randomness here]({{ site.github.url }}/2023/11/16/using-the-t-test-for-effective-testing-of-nondeterministic-AI-systems.html). +Our testing framework treats the application as a black box - it sends a prompt and evaluates the final response without considering how that response was generated. Given the non-deterministic nature of the underlying LLM, relying on string comparisons to judge the response's quality would be futile. Instead, string comparison is reserved solely for verifying the presence of URLs in the response. One of Scottbot’s requirements was to always cite the source of the information it used to generate a response and, as a result, every response was expected to contain a link to the relevant document. To accurately assess the quality of the bot's output, we employ a secondary LLM that evaluates the response against a set of reference information we provide. Moreover, recognizing the inherent variability in the LLM's outputs, we do not rely on single-instance testing. Multiple runs are conducted for each prompt, and the results are assessed on an average basis to account for the LLM's non-deterministic behaviour, thus providing a more robust measure of performance. [You can read more on how statistics can be utilised to address randomness here]({{ site.github.url }}/2023/11/16/using-the-t-test-for-effective-testing-of-nondeterministic-AI-systems.html). ## **New challenges** It should be no surprise that our solution to the non-deterministic nature of our application lead to a new challenge – a significant increase in operational costs. While our project budget was not immediately threatened and we expected that LLM costs would decrease over time, we recognized the importance of resource efficiency, especially since this project served as a training ground for our team. @@ -48,7 +48,7 @@ The first component to isolate is the agent’s decision-making process, which i It is easy to understand if the agent selects the incorrect tool, the bot’s final response will be flawed. Therefore, there is no need to actually invoke the tool or evaluate the final response. Based on this, we designed a test suite to assess the agent’s tool invocation abilities. This suite evaluates which tool the agent chooses and the input it uses it with, in response to a given prompt. As each test need just a single LLM call, they are significantly cheaper and faster. -These tests became particularly useful when we implemented changes that could affect the agent’s decision-making process. For example, the agent type, the system prompt, the tools descriptions, or the agent’s underlying LLM. +These tests became particularly useful when we implemented changes that could affect the agent’s decision-making process. Examples of such changes are modifying the system prompt, the agent type, the tools' descriptions, or the agent’s underlying LLM. ## **Vector store retrieval tests** The next component to isolate was the vector store retrieval process. All internal documents that the bot needs to access to reply to Scott Logic-related questions have been converted into embeddings and been stored in a vector database. During runtime, Scottbot conducts a similarity search based on the user's query to retrieve the most relevant document(s). These documents, along with the user's question, are then passed to the LLM, which attempts to generate an accurate answer. 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zPN8LMfBho%!5Rp5m1^IoA*A=$3!o1RmtHuKud=fsAq%g}SJm)0(;=01ymV*fPZ${N z_nNW-!5uvCUqY1P$o(3+h&=F{9tA!5`;e8ZbCJs22`uI_mzIwFLF@9+Mey@7bsL`= UP|x&Q@W7wlyG(cH>r=!20eC^5r2qf` From fbaa4c173720c33c9e594a70990ecd15454fa1f8 Mon Sep 17 00:00:00 2001 From: Fanis Vlachos <124600483+fvlachos-scottlogic@users.noreply.github.com> Date: Fri, 15 Dec 2023 10:50:46 +0000 Subject: [PATCH 13/16] Modify phrasing the the retrieval chain paragraph --- ...ctive-test-driven-development-in-an-llm-application.markdown | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown index 40b0fc0572..c96bb06a31 100644 --- a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown +++ b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown @@ -62,7 +62,7 @@ This suite was particularly helpful when changes were made that could impact the One of the key advantages of isolating the vector store retrieval process is that it does not rely on any LLM calls – this process is completely deterministic. This results in tests that are not only cost-effective but also rapid, providing us with the ability to swiftly identify and rectify any discrepancies in the retrieval process. ## **Retrieval QA Chain tests** -Finally, we isolated the process of the last part of the Scott Logic tool – the invocation of the Retrieval QA Chain. This process involves providing the LLM with the context fetched from our vector store and the user query and asking it to generate an answer. +Finally, we isolated the last part of the Scott Logic tool – the invocation of the Retrieval QA Chain. This process involves providing the LLM with the context fetched from our vector store and the user query and asking it to generate an answer. ![retrieval_qa_chain.png]({{ site.github.url }}/uploads/retrieval_qa_chain.png) From d2e6721f8bbc0729deea5e4c9ff3b621c52484ee Mon Sep 17 00:00:00 2001 From: Fanis Vlachos <124600483+fvlachos-scottlogic@users.noreply.github.com> Date: Fri, 15 Dec 2023 11:06:18 +0000 Subject: [PATCH 14/16] Removed unnecessary category from categories list --- ...ective-test-driven-development-in-an-llm-application.markdown | 1 - 1 file changed, 1 deletion(-) diff --git a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown b/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown index c96bb06a31..8083efb127 100644 --- a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown +++ 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and rename 2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown to 2023-12-18-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown --- ...tive-test-driven-development-in-an-llm-application.markdown} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename _posts/{2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown => 2023-12-18-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown} (99%) diff --git a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown b/_posts/2023-12-18-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown similarity index 99% rename from _posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown rename to _posts/2023-12-18-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown index 8083efb127..025cb03929 100644 --- a/_posts/2023-12-15-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown +++ b/_posts/2023-12-18-implementing-cost-effective-test-driven-development-in-an-llm-application.markdown @@ -1,6 +1,6 @@ --- title: 'Implementing cost-effective Test-Driven Development in an LLM application' -date: 2023-12-13 14:00:00 Z + categories: - Artificial Intelligence tags:

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