LLM prompt engineering creates overly verbose technical documentation

In the process of generating technical documentation using large language models (LLMs), overly detailed and verbose outputs were produced due to complex prompt engineering practices. These outputs were lengthy and unnatural, resembling documentation rather than concise, developer-friendly explanations.

Issue

LLM-generated technical overviews became excessively verbose, with simple concepts expanded into lengthy explanations (e.g., 150+ words for a concept that could be conveyed in 6 words). For example, a concise explanation like “creates middleware that logs messages” was replaced by verbose paragraphs that resembled formal documentation rather than natural technical writing.

Culprit

The over-engineering of prompts led to unnatural outputs due to:

• Excessive specification: Prompts included detailed examples, style guides, and constraints that forced verbose outputs.
• Aggressive specificity: Instructions like “be specific” and extensive examples taught the LLM to mimic bloated, formal writing patterns.
• Over-constrained examples: Detailed “GOOD” and “BAD” examples reinforced verbosity rather than natural language generation.

The LLM replicated the prompt’s structure and verbosity, leading to unnatural and overly technical outputs.

Solution

Radical Prompt Simplification

Adopted a minimalist prompt approach to achieve concise and natural outputs:

1. Removed over-specification: Eliminated detailed formatting instructions, phrasing templates, and extensive examples.
2. Focused on core requirements: Reduced prompts from 167 lines to 30 lines, retaining only essential structure and critical constraints.
3. Trusted the LLM: Removed over-specific phrasing guidelines, allowing the model to generate natural technical language.

Revised Prompt Structure

The new prompt structure emphasizes:

• Core requirements only: A concise hook question, a specific technique explanation, and a real-world benefit.
• Critical constraints: Guidelines like avoiding forward code references and maintaining a conversational yet technical tone.

Impact

• Conciseness: Outputs reduced from 150+ words to 60-80 words while maintaining technical accuracy.
• Natural language: Achieved a more developer-friendly tone, resembling natural explanations.
• Improved clarity: Simplified outputs focus on essential technical concepts without unnecessary verbosity.

The revised approach ensures that LLM-generated documentation is both concise and natural, improving usability and readability for developers.