I was bothered when chatGPT couldn't even write langchain code.
So I fed a langchain one-pager into chatGPT,
which generated the langchain code,
which can be fed more langchain documentation,
which can generate more langchain code.
I did not write a single line of code.
Life is beautiful again.
Written at 2AM, but serious takeaways:
- With the best LLMs being mostly frozen, prompt is still the best way to alter model states. ChatGPT’s 1-shot learning ability is much more impressive than I thought.
- Ideally, these information should be encoded through fine-tuning (like what CoPilot/CodeComplete probably did) or adapters (e.g. LoRA) on proprietary codebases, but for any new codebase, a “codebase card” is probably the fastest way to solve the cold start problem
python main.py "https://python.langchain.com/en/latest/_sources/getting_started/getting_started.md" "write me a 21 question program using langchain"
it works, take my word for it