Good LLM Validation Is Just Good Validation

The goal of instructor is that i dont write any prompts for you, i just make the openai.ChatCompletion.create call easier to manage and let you return a pass in a pydantic object and return an instance of it,

Whole codebase is 300 lines of code you can copy paste into your own repo into function_calls.py :)

Yeah, obligatory reminder that "chaining" is just function composition. As in:

  SecondQuery(FirstQuery(userInput));

"Advanced" chaining can be as simple as:

  do {
     partial = FirstQuery(input);
     choice = DeciderQuery(partial);
     input = choice ? Query2A(partial) : Query2B(partial);
  } while(!ValidationQuery(input));
  return input; // or print it or whatever

It only looks complex when you need to chain things at runtime through some interface, but that's the usual problem of having to reinvent basic blocks of your language as objects or data structures because your language doesn't let you treat code and data interchangeably. But if you're just experimenting with chains, using the most basic programming building blocks directly - functions, conditionals, loops - is by far the easiest.

This was exactly my experience trying to build a RAG pipeline that answers complex questions by generating sub-questions [1]. The hidden prompts in the complex libraries are quite difficult to get to and then start tweaking, whereas writing my own pipeline boiled down the code to 4 LLM calls. +1 for using the instructor library for quick schema definitions!

[1] https://github.com/pchunduri6/rag-demystified