Instead of using retrieval to enhance ChatGPT, why not use ChatGPT to improve the retrieval?

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I've been spending a lot of time lately building autonomous agents with ReAct and RAG frameworks. The shift from stateless chatbots to stateful systems that can actually execute tools is incredibly us
I love the idea of autonomous coding agents. But one of the quickest ways to hit a wall when setting them up is the infinite context problem. Your context window is finite, but the work you want the a
Building LLM apps is easy, but scaling them without setting a pile of money on fire is hard. You really don't need the massive brainpower of GPT-5 for every single user query. Routing is how we fix th
Building a multi-turn conversational AI is surprisingly easy right now. Evaluating it is incredibly hard. For single-turn tasks, a standard static dataset works fine: you just feed in a prompt and ass
I’ve been reading through a recent paper titled "Is Grep All You Need? How Agent Harnesses Reshape Agentic Search". It’s a provocative piece with a premise I normally love. The authors claim that simp
Given a query, instruct a generative model (ChatGPT) to write a passage to answer the question. The passage may contain factual errors, but it looks like a good answer!
The generated passage is passed through an Encoder (Contriever) to get the embedding of the passage. The encoder acts like a lossy compressor, where the extra (hallucinated) details are filtered out from the embedding.
A vector to search is performed against the corpus embeddings. The most similar real documents are retrieved and returned.
