Author: Adrien Maret
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Chain of thought, the Swiss army knife of the Prompt Engineer
One of the most effective techniques for improving the performance of a prompt is Chain of Thought (CoT). This technique can be applied in all LLM Engineering situations: To fully understand how the Chain of Thought works and how to use it, it is necessary to understand the internal workings of an LLM during the…
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Inside LLMs: understanding tokens
When we talk about LLMs, we find ourselves faced with concepts that are not always necessarily understood. After all, you can use GPT-4 very well without understanding what tokens or temperature are. However, when we seek to go further in the use, it is necessary to understand these different concepts and this is what we…
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Vector databases: chronicle of a foretold death
For several months now, we have heard a lot about these new “Vector Databases” which would be the “memory” of Large-Language-Models. Pinecone ($128M), Qdrant ($28M), Croma ($18M), there are several dozen startups raising millions and fighting in the hypothetical vector search market. In this article, we’ll look at how vector databases are more of a…
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The real revolution brought by Google’s Gemini models
Those who spill ink about the authenticity of Google’s video on Gemini’s multimodal performance or its performance compared to GPT4 are missing the true revolution inside Google announcement. The market is today dominated by large (very large) LLMs. From Mistral to GPT4, including LlaMa 2, all these models whose performance is regularly compared to different…
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A RAG with the HyDE method: augmenting the semantic field
The vector search of a RAG consists of a semantic search in our documents. The original request (the query) is particularly important because it will give us our vector used in the search for the nearest neighbors of the vector plane. The documents that make up our vector database very often come from different sources…
