Retrieval Augmented Generation (RAG) is a very popular framework or class of LLM Application. The basic principle of RAG is to leverage external data sources to give LLMs contextual reference. In the recent past, I wrote much on different RAG approaches and pipelines. But how can we evaluate, measure and quantify the performance of a RAG pipeline?
The skill of Prompt Engineering has been touted as the ultimate skill of the future. But, will prompt engineering be around in the near future? In this article I attempt to decompose how the future LLM interface might look like…considering it will be conversational.
Large Language Models (LLMs) are known to hallucinate. Hallucination is when a LLM generates a highly succinct and highly plausible answer; but factually incorrect. Hallucination can be negated by injecting prompts with contextually relevant data which the LLM can reference.
This working code example using the LangChain framework illustrates how two agents can debate each other after each agent has been assigned a persona and an objective. The agents have access to tools which they can leverage for their response.
The notion of fine-tuning a Large Language Models (LLMs) for very specific generative use-cases is in most instances not feasible. However, due to the flexibility of LLMs, variations in Prompt Engineering can yield astounding results. This article covers a new prompt method which improves LLM results in accuracy and completeness.
Here are six best practices to improve your prompt engineering results. When interacting with LLMs, you must have a vision of what you want to achieve and mimic the initiation of that vision. The process of mimicking is referred to as prompt design, prompt engineering or casting.
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With the emergence of platforms democratizing conversational AI, there’s a smaller hurdle to get a basic NLU model up and running. The real work starts when your conversational AI project is in production and conversations you would have never expected are thrown at you.
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