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docs/tutorials/anatomy_of_jim.md

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 # Anatomy of Jim
 
-
-
+While the actual implementation of classes can be as involve as you like, the top level idea of Jim is rather simple.
+We encourage all extension to `jim` follow this pattern, as it make sure your code can interface with the rest of `jim` without a problem.
+This guide aims to give you a high level overview of what are the important components of Jim, and how they interact with each other.
 ## Likelihood
 
 ### Data
+
+There should be two main ways to get your data into `jim`, either you fetch it from some public database, or generate synthetic data.
+
 ### Model
 
 ## Prior
 
 ## Sampler
 
+The main workhorse under the hood is a machine learning-enhanced sampler named [flowMC](https://flowmc.readthedocs.io/en/main/).
+It shares a similar interface
+For a detail guide to what are all the knobs in `flowMC`, there is a tuning guide for flowMC [here](https://flowmc.readthedocs.io/en/main/configuration/).
+At its core, `flowMC` is still a MCMC algorithm, so the hyperparameter tuning is similar to other popular MCMC samplers such as [emcee](https://emcee.readthedocs.io/en/latest/), namely:
+
+1. If you can, use more chains, especially on a GPU. Bring the number of chains up until you start to get significant performance hit or run out of memory.
+2. Run it longer, in particular the training phase. In fact, most of the computation cost goes into the training part, once you get a reasonably tuned normalizing flow model, the production phase is usually quite cheap. To be concrete, blow `n_loop_training` up until you cannot stand how slow it is.
+
 ## Analysis