Structured data

Study Notes: Controlling LLM Output for Structured Data

**I. Core Problem**

• Large Language Models (LLMs) frequently add unsolicited commentary, headers, or footers when generating structured data (e. g. , JSON, Python code, lists).
• In many applications, the goal is to receive only the raw, usable content, not the model's conversational framing.

**II. The Solution: Combining Techniques**

• To ensure the model outputs only the desired raw content, combine a Stop Sequence with a Pre-filled Assistant Message.
• This technique is highly effective for generating any type of structured data, not just JSON.

**III. Mechanism of Control**

• Pre-filled Assistant Message:
• This message is used to prime the model, signaling that the desired output format has already begun.
• Example: If the goal is raw JSON, the pre-filled message might be json`.
• Stop Sequence:
• This sequence tells the LLM to halt generation immediately upon producing it.
• Example: If the desired output is JSON, the stop sequence would be the closing markdown backticks ( `).
• Process Flow:
• The model starts generation, assuming the pre-filled message is the beginning.
• It generates the required content (the raw data).
• It naturally attempts to close the structure (e. g. , adds the closing backticks).
• The Stop Sequence intercepts this closing sequence, terminating the generation immediately and returning only the content generated in between.

**IV. Post-Processing**

• The resulting raw text may contain extraneous characters, such as extra newlines.
• These can be easily removed by parsing the response as JSON or by using string manipulation functions (e. g. , strip).

Takeaways

• LLMs often include unsolicited commentary or framing when generating structured data like JSON.
• The solution is to combine a Stop Sequence with a Pre-filled Assistant Message to ensure only raw content is returned.
• The Pre-filled Assistant Message primes the model by signaling the desired output format has already begun (e. g. , starting with json`).
• The Stop Sequence tells the LLM to halt generation immediately upon producing it, intercepting the model's natural closing sequence.
• Post-processing, such as using string manipulation or JSON parsing, is necessary to remove any remaining extraneous characters or newlines.