Taming Llms With Structured Outputs Amanvir Com

At DevIgnition 2024, I gave a talk on using structured outputs to get better responses from large language models (LLMs). I was particularly enthusiastic about this topic, as I've been working with LLMs for a while now, and I've found that structured outputs can be a game-changer when integrating LLMs into your software. You can watch a recording of the talk here: If you'd prefer to just skim through the slides instead, you can do that as well: This was a big personal accomplishment for me, as this was my first time giving a technical presentation of this kind, especially on this scale. Posted on Jul 11, 2025 • Edited on Mar 7

Hello, I'm Shrijith. I'm building git-lrc, an AI code reviewer that runs on every commit. It is free, unlimited, and source-available on Github. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product. Let me know if you have any other text you'd like me to clean!uctured output—like JSON, specific types, or regex-compliant text—can feel like herding cats.

Tools like Outlines make this easier by guaranteeing structured output directly during generation, even for large, multi-part responses. This post dives into how Outlines works, why it’s a game-changer for developers, and how you can use it to avoid parsing nightmares. We’ll explore code examples, key concepts, and practical tips to make your LLM projects more reliable. LLMs often generate freeform text, which is great for creative tasks but a headache when you need structured data like JSON, integers, or specific formats. Parsing raw LLM output is error-prone—think broken JSON, inconsistent formats, or extra fluff. Outlines solves this by enforcing structure at the generation step, not after.

This means: This approach is perfect for tasks like API response formatting, customer support ticket parsing, or extracting structured data from text. Let’s break down how it works. In limits, there is freedom. Creativity thrives within structure. While Language Models excel at generating human-like text, they face challenges when tasked with producing structured output in a consistent manner [Shorten et al., 2024, Tang et al., 2024].

This limitation becomes particularly problematic when integrating LLMs into production systems that require well-formatted data for downstream processing through databases, APIs, or other software applications. Even carefully crafted prompts cannot guarantee that an LLM will maintain the expected structure throughout its response. But what user needs drive the demand for LLM output constraints? In a recent work by Google Research [Liu et al., 2024], the authors explored the user need for constraints on the output of large language models, drawing on a survey of 51 industry professionals... User needs can be broadly categorized as follows: 1.

Improving Developer Efficiency and Workflow Reducing Trial and Error in Prompt Engineering: Developers find the process of crafting prompts to elicit desired output formats to be time-consuming, often involving extensive testing and iteration. LLM output constraints could make this process more efficient and predictable. published on 2025-03-15 15:03:00 #Taming #LLMs #Structured #Outputs #Amanvir #Parhar Taming LLMs with Structured Outputs Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) by enabling machines to understand and... However, these models often require significant computing resources and can be difficult to train and scale. # # # # # #

🚀 Day 4 – Key AI Concepts Made Simple. AI might sound complicated, but let’s break it down with easy analogies 👇 🔹 Machine Learning (ML): Like teaching a child with flashcards — the more examples you give, the smarter they get. 🔹 Deep Learning (DL): Imagine layers of detectives working together — each one focusing on a tiny detail to solve the big mystery. 🔹 Natural Language Processing (NLP): Think of it as a translator — helping computers understand and talk like humans. (Yes, like me chatting with you 😉). 🔹 Computer Vision (CV): Like giving eyes to a computer — so it can "see" and recognize images, faces, and objects.

✨ The goal? To make machines think, learn, and act closer to us humans. 👉 Stay tuned for tomorrow’s post where we’ll dive into real-world examples of these concepts! #AI #MachineLearning #DeepLearning #NLP #ComputerVision #TechMadeSimple #AIForBeginners 🚀 Day 4 – Key AI Concepts Made Simple. AI might sound complicated, but let’s break it down with easy analogies 👇 🔹 Machine Learning (ML): Like teaching a child with flashcards — the more examples you give, the smarter they get.

🔹 Deep Learning (DL): Imagine layers of detectives working together — each one focusing on a tiny detail to solve the big mystery. 🔹 Natural Language Processing (NLP): Think of it as a translator — helping computers understand and talk like humans. (Yes, like me chatting with you 😉). 🔹 Computer Vision (CV): Like giving eyes to a computer — so it can "see" and recognize images, faces, and objects. ✨ The goal? To make machines think, learn, and act closer to us humans.

👉 Stay tuned for tomorrow’s post where we’ll dive into real-world examples of these concepts! #AI #MachineLearning #DeepLearning #NLP #ComputerVision #TechMadeSimple #AIForBeginners 📌 What Is Natural Language Processing? Natural Language Processing, or NLP for short, is broadly defined as the automatic manipulation of natural language, like speech and text, by software. The study of natural language processing has been around for more than 50 years and grew out of the field of linguistics with the rise of computers. In this post, you will discover what natural language processing is and why it is so important.

After reading this post, you will know: -What natural language is and how it is different from other types of data. -What makes working with natural language so challenging. -Where the field of NLP came from and how it is defined by modern practitioners. 🔗 https://lnkd.in/dH6MkxKm #AI #MachineLearning #ML #NLP #NaturalLanguageProcessing **Natural Language Processing: The Engine Behind Language-Aware AI In recent years, Natural Language Processing (NLP) has transformed from simple keyword detection into deep language understanding — thanks to powerful architectures like Transformers and Large... How it works: At its core, NLP enables machines to process and generate human language using deep learning.

The Transformer architecture introduced by Google’s “Attention is All You Need” paper (2017) revolutionized the field by replacing sequential models (RNNs, LSTMs) with self-attention mechanisms, allowing parallel processing and better context understanding. Modern NLP models, such as BERT, GPT, and T5, leverage billions of parameters trained on vast text corpora. This enables them to perform a wide range of tasks: - Text classification & sentiment analysis - Named Entity Recognition (NER) - Question answering - Machine translation - Text summarization & generation Today’s LLMs... The future of NLP isn’t just about understanding words — it’s about understanding intent, context, and meaning at a human level. #NLP #MachineLearning #DeepLearning #ArtificialIntelligence #Transformers #LLMs #DataScience #TechInnovation Chapter 2: “Structured Output” of the book Taming LLMs is now available for review.

Visit github repo to access Chapter in the following formats: The pdf format is recommended as it contains the highest quality copy. Please share feedback via one of the following: Send a message via substack, linkedin or twitter Large language models (LLMs) are like wild animals — powerful and versatile, but unpredictable and potentially dangerous. This makes deploying robust LLM applications challenging.

In this blog post, we present the notion of structured text generation, which enables practitioners to “tame” LLMs by imposing formatting constraints on their outputs. More precisely, we will: Structured text generation methods are available for four main categories of formatting constraints. The simplest one is restricting the LLM’s outputs to a predefined set of options. For example, when implementing an LLM-as-a-judge approach, we may want to generate a score from 1 to 5, in which case we would expect only five answers: “1”, “2”, “3”, “4”, and “5”. More general constraints can be expressed through regular expressions.

The most typical example is the generation of a JSON object adhering to a specific JSON schema. For example, if we perform sentiment analysis on online reviews, the expected LLM response may be a JSON object with two properties: “sentiment” which is a string with three potential values (“positive”, “negative”, and... An even wider family of constraints is based on formal grammars, which are particularly interesting when we want to obtain syntactically correct computer code through an LLM. Finally, formatting constraints can take the form of templates, which are dynamic, fill-in-the-blank texts whose placeholders are meant to be filled by an LLM. A talk I gave at DevIgnition 2024, a Washington D.C.-based developer conference. A talk I gave at Startup Shell, the University of Maryland's flagship startup incubator.

One home run is much better than two doubles. Case Study I: Content Chunking with Contextual Linking Case Study II: Quiz Generation with Citations While advances in long-context language models (LCs) [Lee et al., 2024] have expanded the amount of information these LLMs can process, significant challenges remain in managing and effectively utilizing extended data inputs: LLMs are sensitive to input formatting and structure, requiring careful data preparation to achieve optimal results [He et al., 2024, Liu et al., 2024, Tan et al., 2024].

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