Resolved

AI text replacement system increases file size instead of improving conciseness

AI/ML
NLP
Text Processing
Automation
Content Generation
Python
Prompt Engineering
Optimization

REPLACE instructions in automated content generation were technically replacing text but producing longer outputs | Root cause was missing length constraints in evaluator prompts causing 11-32% file size growth per iteration

Working with an automated video generation system’s text replacement pipeline, the system used evaluator prompts to guide iterative text refinement via REPLACE operations. The goal was to improve script quality while maintaining or reducing file size.

Issue

The text replacement system generated progressively larger files despite adhering to the ‘REPLACE NOT ADD’ principle. Observed file size growth included:

  • 11% increase (2,347→2,607 bytes).
  • 32% character increase in sample replacements (71→94 characters).
  • Examples showed semantic improvements but violated length constraints.

Culprit

The evaluator prompts lacked explicit length constraints, focusing solely on semantic improvements without enforcing length limits. This led to:

  1. Replacements that were 23-100% longer than originals.
  2. Examples demonstrating semantic replacement but ignoring word/character counts.
  3. No validation logic comparing original and replacement text lengths.

Solution

Length Enforcement Principle

Introduced explicit constraints in evaluator prompts to ensure replacements are equal or shorter in length:

  • Emphasized achieving insight through better words, not more words.
  • Required word/character-aware replacements with clear constraints.

Validation Metrics

Implemented three-layer validation to prevent expansion:

  1. Specific feedback: Replacements explicitly limited by word/character counts.

    • Example: Replace a 50-word paragraph with ≤50 words.
    • Example: Remove redundant paragraphs instead of expanding.

  2. Repetition detection: Prevented redundant expansions by enforcing concise replacements.

  3. Hard length constraints: Programmatic checks to ensure len(replacement) ≤ len(original).

Impact

The solution created a structured approach to enforcing conciseness:

  • Iterative file size: Ensured each iteration maintained or reduced file size.
  • Character delta: Required non-positive growth (Δ ≤ 0).
  • Mandatory word counts: Evaluator feedback explicitly included word limits.

This approach ensured that replacements improved content quality while adhering to strict length constraints, achieving the original goal of reducing file size.