LangChain for Absolute Beginners - Part 4: RAG - Teaching Your Agent to Read Documents=
Article summary
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Every model has a knowledge cutoff, and none of them have ever seen your company's internal wiki, your PDFs, or last week's meeting notes. Retrieval-Augmented Generation (RAG) solves this by fetching relevant chunks of your documents at question time and feeding them into the model's context before it answers. By the end of this article, your agent from Part 3 will be able to answer questions about a document it was never trained on. Recap: The Series So Far Part 1 - What LangChain is &…
1Key Takeaways
- Every model has a knowledge cutoff, and none of them have ever seen your company's internal wiki, your PDFs, or last week's meeting notes.
- Retrieval-Augmented Generation (RAG) solves this by fetching relevant chunks of your documents at question time and feeding them into the model's context before it answers.
- By the end of this article, your agent from Part 3 will be able to answer questions about a document it was never trained on.
- Recap: The Series So Far Part 1 - What LangChain is &….
2AIWedia Score
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3Why it matters
Prompt and agent patterns spread fast; staying current saves time and token cost. DEV — Prompt Engineering reports that every model has a knowledge cutoff, and none of them have ever seen your company's internal wiki, your PDFs, or last week's meeting notes.
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