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Are you developing an application and looking to integrate large language model (LLM) features? With multiple options like GPT, Gemini, Claude, and open-source models from Hugging Face, choosing the right solution can be overwhelming. Each model offers unique strengths, from GPT's versatile text generation to Claude's detailed descriptions, and an open-source model's flexibility. Integrating LLM features can significantly enhance your application by providing capabilities such as natural language understanding, text generation, and intelligent automation. To make an informed decision, it’s essential to evaluate your application's specific needs, compare model performances,…

In this article, we are going to talk about three tools that can, at least in theory, force any local LLM to produce structured output: LM Format Enforcer, Outlines, and Guidance. After a short description of each tool, we will evaluate their performance on a few test cases ranging from book recommendations to extracting information from HTML. And the best for the end, we will show you how forcing LLMs to produce a structured output can be used to solve a very common problem in many businesses: extracting structured records from free-form text.

Me and many of my colleagues at profiq use Notion for note-taking and work organization. Our workspaces contain a lot of knowledge about our work, plans, or the articles or books we read. At some point, a thought came to my mind: couldn’t we use all this knowledge to come up with project ideas suited to our skills and interests?

It has been over a year since OpenAI introduced ChatGPT and brought the power of AI and large language models (LLMs) to the average consumer. But we could argue that introducing APIs for seamlessly integrating large language models into apps developed by companies and independent hackers all over the world can be the true game changer in the long term. Developers are having heated discussions about how we can utilize this technology to develop truly useful apps that provide real value instead of just copying what OpenAI does. We want to contribute to this discussion by showing you how we think about developing autonomous agents at profiq. But first a bit of background.