Building Question Answering Models with LLMs: A Step-by-Step Guide
📰 Dev.to AI
Build a retrieval-augmented question answering model using LLMs and local embeddings to answer questions from a private text corpus
Action Steps
- Install Python 3.10 or newer
- Run pip install openai sentence-transformers numpy to install required libraries
- Configure a local embedding model for retrieval
- Set up an Oxlo.ai LLM for generation
- Build a retrieval-augmented question answering agent using the installed libraries and models
Who Needs to Know This
Support teams, legal researchers, and data scientists can benefit from this model to provide grounded answers without fine-tuning
Key Insight
💡 Retrieval-augmented question answering models can provide grounded answers without fine-tuning
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Build a QA model with LLMs and local embeddings to answer questions from a private text corpus #LLMs #QA
Key Takeaways
Build a retrieval-augmented question answering model using LLMs and local embeddings to answer questions from a private text corpus
Full Article
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</co
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