diff --git a/src/functions/summary/handler.ts b/src/functions/summary/handler.ts
index 2ce67e7..24ad9c2 100644
--- a/src/functions/summary/handler.ts
+++ b/src/functions/summary/handler.ts
@@ -9,9 +9,8 @@ import schema from './schema';
 import { getSummary } from '@libs/chat-gpt';
 import { determineTabFromUrl } from '@libs/tab-resolver';
 
-const INPUT_SIZE_FREQUENT = 1000;
+const INPUT_SIZE_FREQUENT = 1500;
 const INPUT_SIZE_SPARSE = INPUT_SIZE_FREQUENT * 10;
-const MAX_COMMENTS = 1000;
 
 const summary: ValidatedEventAPIGatewayProxyEvent<typeof schema> = async (
   event
@@ -25,7 +24,7 @@ const summary: ValidatedEventAPIGatewayProxyEvent<typeof schema> = async (
     : INPUT_SIZE_FREQUENT;
   try {
     const client = await getAuthClient();
-    let data = await getLastNComments(
+    const data = await getLastNComments(
       client,
       commentBatchSize,
       pageURL,
@@ -37,15 +36,17 @@ const summary: ValidatedEventAPIGatewayProxyEvent<typeof schema> = async (
         dataSize: 0
       });
     }
-    if (data.length > MAX_COMMENTS) {
-      data = data.slice(data.length - MAX_COMMENTS);
-    }
-    const comments = data.map((v) => v[columnMap.comment.index].trim());
-    const dataSummary = await getSummary(comments, promptCustomization);
+    const comments = data.map((v) =>
+      v[columnMap.comment.index].slice(0, 500).trim()
+    );
+    const { dataSummary, commentCount } = await getSummary(
+      comments,
+      promptCustomization
+    );
     return formatJSONResponse({
       message: 'Success',
       dataSummary,
-      dataSize: comments.length,
+      dataSize: commentCount,
       dataStart: data[0][columnMap.timestamp.index],
       dataEnd: data[data.length - 1][columnMap.timestamp.index]
     });
diff --git a/src/libs/chat-gpt.test.ts b/src/libs/chat-gpt.test.ts
index 81752cf..7024a40 100644
--- a/src/libs/chat-gpt.test.ts
+++ b/src/libs/chat-gpt.test.ts
@@ -18,39 +18,64 @@ jest.mock('@azure/openai', () => {
 describe('getSummary', () => {
   const comments = ['Test Comment 1', 'Test Comment 2'];
   const userContent = `---\nTest Comment 1\nTest Comment 2---`;
+  const testPromptCustomization = 'test prompt';
 
   afterEach(() => {
     jest.clearAllMocks();
   });
 
-  it('should use the expected arguments and return a string response', async () => {
-    const testPrompt = 'test prompt';
-    const systemContent = `You are an assistant designed to find the most common themes in a large dataset of free text. Users will send a list of comments ${testPrompt}, where each line represents one comment. You will find the 10 most common themes in the data, and for each theme, you will include a theme title, theme description, and 3 real comments (actually in the dataset, not generated) that fit the given theme. Your output will be in the following structured valid JSON format: {"themes":[{"title":"title 1","description":"description 1","actualComments":["real comment 1","real comment 2","real comment 3"]},{"title":"title 2","description":"description 2","actualComments":["real comment 1","real comment 2","real comment 3"]}, ...]}. Make sure that the 3 comments are in the user-provided list of comments, not generated. Make sure the output is in valid JSON format, and do not add trailing commas.`;
+  it('should use the expected arguments and return the expected response', async () => {
+    const systemContent = `You are an assistant designed to find the most common themes in a large dataset of free text. Users will send a list of comments ${testPromptCustomization}, where each line represents one comment. You will find the 10 most common themes in the data, and for each theme, you will include a theme title, theme description, and 3 real comments (actually in the dataset, not generated) that fit the given theme. Your output will be in the following structured valid JSON format: {"themes":[{"title":"title 1","description":"description 1","actualComments":["real comment 1","real comment 2","real comment 3"]},{"title":"title 2","description":"description 2","actualComments":["real comment 1","real comment 2","real comment 3"]}, ...]}. Make sure that the 3 comments are in the user-provided list of comments, not generated. Make sure the output is in valid JSON format, and do not add trailing commas.`;
     const deployment_id = 'gpt-35-turbo-16k';
     const prompt = [
       { role: 'system', content: systemContent },
       { role: 'user', content: userContent }
     ];
-    const result = await getSummary(comments, testPrompt);
+    const result = await getSummary(comments, testPromptCustomization);
     expect(MOCK_CHAT_COMPLETIONS).toHaveBeenCalledTimes(1);
     const mockChatCompletionsArgs = MOCK_CHAT_COMPLETIONS.mock.calls[0];
     expect(Array.isArray(mockChatCompletionsArgs)).toBe(true);
     expect(mockChatCompletionsArgs[0]).toBe(deployment_id);
     expect(mockChatCompletionsArgs[1][0]).toMatchObject(prompt[0]);
     expect(mockChatCompletionsArgs[1][1]).toMatchObject(prompt[1]);
-    expect(result).toBe('mocked response');
+    expect(result).toStrictEqual({
+      commentCount: 2,
+      dataSummary: 'mocked response'
+    });
   });
 
-  it('should return "{}" and not call OpenAIClient when there are no comments', async () => {
+  it('should return an object showing no summary from 0 comments and not call OpenAIClient when comments are an empty array', async () => {
     const comments = [];
-    const pageURL = 'https://uistatus.dol.state.nj.us/';
-    const result = await getSummary(comments, pageURL);
+    const result = await getSummary(comments, testPromptCustomization);
     expect(OpenAIClient).toHaveBeenCalledTimes(0);
-    expect(result).toBe('{}');
+    expect(result).toStrictEqual({
+      commentCount: 0,
+      dataSummary: 'No data found'
+    });
   });
 
-  describe('getSummary with a different prompts prompts', () => {
-    const cases = ['test prompt', ''];
+  it('should reduce number comments if content in comments is estimated to exceed token limit', async () => {
+    const comments = new Array(10000).fill(
+      'This is an example comment that a has been submitted by a user as feedback' // comment length = 74 char
+    );
+    // example comment array that will exceed the token limit check in reduceCommentToNotExceedTokenLimit
+    // comments array joined as a string will total 10000 * (74 char + 1 space char) = 75000 characters
+    // estimated character limit for input is 57536 (14384 available tokens * 4 characters per token)
+    // 57536 % 75 characters (comment + ' ') = 767 comments that can be included in the input
+
+    const expectedReducedCommentLength = 767;
+    MOCK_CHAT_COMPLETIONS.mockResolvedValueOnce({
+      choices: [{ message: { content: 'Mock summary' } }]
+    });
+    const result = await getSummary(comments, testPromptCustomization);
+    expect(result).toEqual({
+      dataSummary: 'Mock summary',
+      commentCount: expectedReducedCommentLength
+    });
+  });
+
+  describe('getSummary should correctly utilize different prompt customizations', () => {
+    const cases = [testPromptCustomization, 'a different prompt'];
     it.each(cases)(
       'should correctly include the prompt in the system content',
       async (prompt) => {
@@ -63,7 +88,39 @@ describe('getSummary', () => {
     );
   });
 
-  it('should throw an error on failure', async () => {
+  it('should retry if the context length is exceeded and error is thrown with message rearding max content', async () => {
+    const comments = new Array(100).fill('This is a comment');
+    MOCK_CHAT_COMPLETIONS.mockRejectedValueOnce({
+      message: "This model's maximum context length"
+    });
+    MOCK_CHAT_COMPLETIONS.mockResolvedValueOnce({
+      choices: [{ message: { content: 'Mock summary after retry' } }]
+    });
+
+    const result = await getSummary(comments, testPromptCustomization);
+    expect(result).toEqual({
+      dataSummary: 'Mock summary after retry',
+      commentCount: comments.length - 50
+    });
+    expect(MOCK_CHAT_COMPLETIONS).toHaveBeenCalledTimes(2);
+  });
+
+  it('should retry if the context length is exceeded and only retry the max amount of times and finally throw error if context length is still exceeding limit', async () => {
+    const comments = new Array(1000).fill('This is a comment');
+    const maxContextErrorMessage = `This model's maximum context length`;
+    const maxRetries = 5;
+    for (let i = 0; i < maxRetries; i++) {
+      MOCK_CHAT_COMPLETIONS.mockRejectedValueOnce({
+        message: maxContextErrorMessage
+      });
+    }
+    await expect(getSummary(comments, testPromptCustomization)).rejects.toThrow(
+      `Azure OpenAI getChatCompletions failed after ${maxRetries} retries with error: ${maxContextErrorMessage}`
+    );
+    expect(MOCK_CHAT_COMPLETIONS).toHaveBeenCalledTimes(maxRetries);
+  });
+
+  it('should throw an error when other non-context length errors occur', async () => {
     const testErrorMessage = 'Test Error';
     (OpenAIClient as jest.Mock).mockImplementation(() => ({
       getChatCompletions: jest
@@ -71,8 +128,7 @@ describe('getSummary', () => {
         .mockRejectedValue(new Error(testErrorMessage))
     }));
     const expectedErrorMessage = `Azure OpenAI getChatCompletions failed with error: ${testErrorMessage}`;
-    const pageURL = 'https://www.nj.gov/labor/myleavebenefits/';
-    await expect(getSummary(comments, pageURL)).rejects.toThrow(
+    await expect(getSummary(comments, testPromptCustomization)).rejects.toThrow(
       expectedErrorMessage
     );
   });
diff --git a/src/libs/chat-gpt.ts b/src/libs/chat-gpt.ts
index 9293604..6874efd 100644
--- a/src/libs/chat-gpt.ts
+++ b/src/libs/chat-gpt.ts
@@ -15,17 +15,35 @@ const PARAMETERS = {
   n: 1
 };
 const DEPLOYMENT_ID = 'gpt-35-turbo-16k';
+const MAX_TOKENS_INPUT_OUTPUT = 16384;
+const CHARACTERS_PER_TOKEN = 4;
+const COMMENT_SLICE_VALUE = 50
 
-export async function getSummary(comments: string[], promptCustomText: string) {
-  if (comments.length === 0) {
-    return '{}';
+function reduceCommentToNotExceedTokenLimit(comments: string[]): string[] {
+  const availableInputTokens = MAX_TOKENS_INPUT_OUTPUT - PARAMETERS.maxTokens;
+  const totalCommentCharacters = comments.join(' ').length;
+  const estimatedTokensForComments =
+    totalCommentCharacters / CHARACTERS_PER_TOKEN;
+  if (estimatedTokensForComments <= availableInputTokens) {
+    return comments;
+  } else {
+    let currentlyUsedTokens = 0;
+    let currentlyJoinedString = '';
+    for (let i = comments.length - 1; i >= 0; i--) {
+      currentlyJoinedString += `${comments[i]} `;
+      currentlyUsedTokens = currentlyJoinedString.length / CHARACTERS_PER_TOKEN;
+      if (currentlyUsedTokens > availableInputTokens) {
+        return comments.slice(i + 1);
+      }
+    }
   }
-  const client = new OpenAIClient(ENDPOINT, new AzureKeyCredential(API_KEY), {
-    apiVersion: API_VERSION
-  });
+}
 
-  const promptProgram = promptCustomText || '';
-  const systemContent = `You are an assistant designed to find the most common themes in a large dataset of free text. Users will send a list of comments ${promptProgram}, where each line represents one comment. You will find the 10 most common themes in the data, and for each theme, you will include a theme title, theme description, and 3 real comments (actually in the dataset, not generated) that fit the given theme. Your output will be in the following structured valid JSON format: {"themes":[{"title":"title 1","description":"description 1","actualComments":["real comment 1","real comment 2","real comment 3"]},{"title":"title 2","description":"description 2","actualComments":["real comment 1","real comment 2","real comment 3"]}, ...]}. Make sure that the 3 comments are in the user-provided list of comments, not generated. Make sure the output is in valid JSON format, and do not add trailing commas.`;
+function generatePrompt(
+  comments: string[],
+  promptCustomText: string
+): ChatRequestMessage[] {
+  const systemContent = `You are an assistant designed to find the most common themes in a large dataset of free text. Users will send a list of comments ${promptCustomText}, where each line represents one comment. You will find the 10 most common themes in the data, and for each theme, you will include a theme title, theme description, and 3 real comments (actually in the dataset, not generated) that fit the given theme. Your output will be in the following structured valid JSON format: {"themes":[{"title":"title 1","description":"description 1","actualComments":["real comment 1","real comment 2","real comment 3"]},{"title":"title 2","description":"description 2","actualComments":["real comment 1","real comment 2","real comment 3"]}, ...]}. Make sure that the 3 comments are in the user-provided list of comments, not generated. Make sure the output is in valid JSON format, and do not add trailing commas.`;
   const userContent = '---\n' + comments.join('\n') + '---';
   const prompt: ChatRequestMessage[] = [
     {
@@ -34,17 +52,61 @@ export async function getSummary(comments: string[], promptCustomText: string) {
     },
     { role: 'user', content: userContent }
   ];
+  return prompt;
+}
 
-  try {
-    const result = await client.getChatCompletions(
-      DEPLOYMENT_ID,
-      prompt,
-      PARAMETERS
-    );
-    return result.choices[0].message.content;
-  } catch (e) {
+export async function getSummary(comments: string[], promptCustomText: string) {
+  if (comments.length === 0) {
+    return {
+      dataSummary: 'No data found',
+      commentCount: 0
+    };
+  }
+  const client = new OpenAIClient(ENDPOINT, new AzureKeyCredential(API_KEY), {
+    apiVersion: API_VERSION
+  });
+
+  comments = reduceCommentToNotExceedTokenLimit(comments);
+  let prompt = generatePrompt(comments, promptCustomText);
+  const maxRetries = 5;
+  let retries = 0;
+  let lastError: Error | null = null;
+  let summaryGenerated = false;
+  let resultData = {
+    dataSummary: 'No data found',
+    commentCount: 0
+  };
+
+  while (comments.length > 0 && retries < maxRetries) {
+    try {
+      const result = await client.getChatCompletions(
+        DEPLOYMENT_ID,
+        prompt,
+        PARAMETERS
+      );
+      resultData = {
+        dataSummary: result.choices[0].message.content,
+        commentCount: comments.length
+      };
+      summaryGenerated = true;
+      break;
+    } catch (e) {
+      if (e.message.includes("This model's maximum context length")) {
+        comments = comments.slice(COMMENT_SLICE_VALUE);
+        prompt = generatePrompt(comments, promptCustomText);
+        retries += 1;
+        lastError = e;
+      } else {
+        throw Error(
+          `Azure OpenAI getChatCompletions failed with error: ${e.message}`
+        );
+      }
+    }
+  }
+  if (!summaryGenerated && lastError) {
     throw Error(
-      `Azure OpenAI getChatCompletions failed with error: ${e.message}`
+      `Azure OpenAI getChatCompletions failed after ${maxRetries} retries with error: ${lastError.message}`
     );
   }
+  return resultData;
 }