LLM Dev Frameworks

The main takeaway is that the best LLMs framework to use depends on your specific project goals, resources, and constraints - building your own gives full control but requires more time and technical skill, OpenAI provides quick and easy access but less customization, Langchain balances customization and ease of use, and Anthropic Constitutional AI minimizes potential harms.

Summary

• When using large language models (LLMs) for an application, there are various framework options:
  • Build your own from scratch - full control and customization but very time/resource intensive
  • Use OpenAI assistants like GPT-3.5 - quick to deploy but limited flexibility and dependence on OpenAI
  • Use Langchain - balances customization and ease of use for different LLM integrations
  • Use Anthropic Constitutional AI - minimizes potential harms
  • Use Lama Index - best for data-heavy retrieval-augmented generation (RAG) applications
• Building your own framework allows full ownership and customization but requires substantial technical expertise, time and resources.
• OpenAI assistants provide streamlined accessibility to quickly build proofs of concept that leverage LLMs, but offer less flexibility.
• Langchain makes it easier to do prompt engineering, parse outputs, and maintain user context for conversations.
• Lama Index stands out in robust data handling capabilities, indexing, storage, and retrieval features - ideal for RAG systems.
• There is no one-size-fits-all solution - the best framework depends on the specific project goals, resources, and constraints.