Langchain Is Pointless
The reason why Langchain is pointless is that it's trying to solve problems on top of technical foundations that just cannot support it.<p>The #1 learning is that there is no reusability with the current generation of LLMs. We're using GPT-4 and 3.5T exclusively.<p>Over the last several months, my team has been building several features using highly sophisticated LLM chains that do all manner of reasoning. The ultimate outputs are very human-like to the point where there is some private excitement that we've built an AGI.<p>Each feature requires very custom handwritten prompts. Each step in the chain requires handwritten prompts. The input data has to be formatted a very specific way to generate good outputs for that feature/chain step. The part around setting up a DAG orchestration to run these chains is like 5% of the work. 95% is really just in the prompt tuning and data serialization formats.<p>None of this stuff is reusable. Langchain is attempting to set up abstractions to reuse everything. But what we end up with a mediocre DAG framework where all the instructions/data passing through is just garbage. The longer the chain, the more garbage you find at the output.<p>We briefly made our own internal Langchain. We tore it down now. Again not that our library or Langchain was bad engineering. It's just not feasible on top of the foundation models we have right now.