Generating Structured Outputs from LLMs: A Guide for Engineers

Generating structured outputs from large language models (LLMs) is essential for integrating these models into software systems, where unstructured text is insufficient. While chat interfaces work well for human users, applications require predictable, machine-readable data formats like JSON. This article explores three key approaches to achieving structured outputs: relying on API providers, prompting and reprompting strategies, and constrained decoding. The first approach leverages API providers such as OpenAI and Google Gemini, which support schema-based output generation using Pydantic models. These services automatically enforce structure, simplifying integration. However, this method ties developers to specific providers, limiting flexibility and exposing them to pricing changes. It also abstracts the underlying mechanics, making debugging and optimization more difficult. The second approach uses prompting and reprompting. Here, users instruct the model via system prompts to follow a specific format, often with examples. The model’s response is then parsed to validate structure. If parsing fails, the system retries with a refined prompt. Libraries like Instructor automate this process, supporting multiple languages and providers. While convenient, this method incurs additional costs due to repeated API calls. To manage expenses, developers should set hard limits on retry attempts. The third and most powerful method is constrained decoding. Unlike prompting, it guarantees structured output without retries. It works by leveraging the autoregressive nature of LLMs—generating one token at a time. At each step, the model’s output is restricted to only those tokens that maintain adherence to the defined schema. This is achieved by transforming the schema into a regular expression (RegEx), which is then compiled into a Deterministic Finite Automaton (DFA). The DFA defines valid transitions between states based on input tokens. During generation, the system tracks the current state and only allows tokens that correspond to valid outgoing transitions. By zeroing out logits for invalid tokens before softmax, the model is forced to produce only valid sequences. This approach is highly efficient—no extra API calls are needed—and supports open-source models, making it ideal for scalable, cost-effective applications. A leading library for this technique is Outlines, which supports Pydantic models and RegEx patterns, and integrates with providers like OpenAI, Anthropic, Ollama, and vLLM. Its ease of use and reliability make it a top choice for engineers building robust LLM-powered systems. In conclusion, while API-based and prompting methods offer quick wins, constrained decoding stands out for its reliability, cost efficiency, and flexibility. For developers aiming to build production-grade applications, mastering constrained decoding is a strategic advantage. Resources like the deeplearning.ai course by dottxt provide hands-on training in these techniques, helping engineers implement structured outputs effectively.