Our technical writing team is using LLMs to draft API documentation, but we’re seeing frequent hallucinations regarding parameter types. Can a more structured prompt engineering strategy, like Chain-of-Thought or few-shot prompting, solve this, or is it a limitation of the current models we use?
3 answers
Hallucinations often occur when the model lacks specific constraints. To fix this, your prompt engineering should include a "grounding" step. Provide the model with the exact JSON schema or source code as a reference and explicitly instruct it to "only use provided information." We implement few-shot prompting by giving the model three perfect examples of our documentation style before the actual task. This reduced our error rate by about 45% in our last audit in late 2024. Also, try asking the model to "verify its own output against the source" as a final step in the prompt.
Have you tried using a multi-step prompt engineering pipeline where one prompt generates the draft and a second "critic" prompt specifically looks for technical inconsistencies?
Try the "Chain-of-Thought" technique. Ask the AI to explain its reasoning for each parameter type before writing the final documentation.
Gregory is right; CoT is essential. I've found that forcing the model to "think step-by-step" makes the prompt engineering much more reliable for technical tasks.
That’s a brilliant suggestion, Jeffrey. We currently use a single long prompt, but breaking it into a "Generator" and a "Reviewer" sounds like a much more robust prompt engineering workflow. I’ll try setting up a script to pass the output from the first LLM call into a second one that has the role of a "Senior Technical Editor" to see if that catches the parameter errors we've been seeing.