Building Question Answering Models with LLMs: A Step-by-Step Guide
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We are going to build a retrieval-augmented question answering agent that answers questions from a private text corpus. This is useful for support teams, legal researchers, or anyone who needs grounded answers without fine-tuning. We will use a local embedding model for retrieval and an Oxlo.ai LLM for generation. What you'll need Python 3.10 or newer. The OpenAI SDK and a few helpers: pip install openai sentence-transformers numpy . An Oxlo.ai API key from https://portal.oxlo.ai . Step 1: Set…
1Key Takeaways
- We are going to build a retrieval-augmented question answering agent that answers questions from a private text corpus.
- This is useful for support teams, legal researchers, or anyone who needs grounded answers without fine-tuning.
- We will use a local embedding model for retrieval and an Oxlo.ai LLM for generation.
- What you'll need Python 3.10 or newer.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — AI reports that we are going to build a retrieval-augmented question answering agent that answers questions from a private text corpus.
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