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Book Overview & Buying
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Table Of Contents
LLMs in Enterprise
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In this chapter, we covered key techniques to enhance how LLMs handle context, making them more useful across industries like healthcare, finance, and customer service. We looked at why adapting models to specific contexts is essential for delivering relevant, precise responses. Key methods include using external memory systems to store important data as embeddings and allowing LLMs to access relevant information on demand.
We also explored retrieval-augmented generation (RAG) and hybrid search techniques, combining traditional search with semantic search to improve accuracy. We discussed prompt engineering tips to help design effective prompts, minimizing issues like ambiguous or irrelevant responses.
In the next chapter, the focus will shift to evaluating LLMs in production settings and establishing feedback loops. This will include methodologies to measure the performance of these models under real-world conditions and how feedback mechanisms can be integrated to...