How the tech news aggregator daily.dev created an AI search, including using an LLM Gateway to streamline and optimize interactions with LLMs.

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Daily.dev launched the beta version of their AI-powered Search, which aims to deliver concise, accurate answers to technical queries. The system follows a workflow involving query generation, candidate generation, scraping, context building, and answer generation. They integrated LLMs using the LLM Gateway, which streamlines interactions and offers foundational building blocks. The orchestrator, Magni, executes the workflow and provides traceability and debugging. Challenges include managing stateful services, cost analysis, optimizing performance, and prompt engineering.

How Daily Dev Built an AI Search Using an LLM Gateway

<p>I’ve had similar experiences with the difficulties specified in the blog. Building a system like this necessitates careful consideration of performance, cost, and timeliness. It is essential to optimize the system for speed and efficiency, as well as to manage expenses and craft effective prompts. The blog accurately reflects the complexities involved in developing LLM applications. But I am very curious about how you evaluated and selected the LLM models for your system? What factors did you consider?</p>


<p>Works better than I expected for prelim discovery / high level information. I do like being able to extend conversations more naturally as they evolve, which would be a nice feature to augment this technology with in the future. I’m sure there’s a reason for this since the more prompt flexibility we have, the more control over unexpected output users can finagle, which is probably considered dangerous from an AI development standpoint.</p>
