stop bleeding money: why your llm system needs better input guardrails

November 17, 2024

Introduction

this post discusses the iterative development of input guardrails for large language models (llms). llms have two types of guardrails: input and output. while both are crucial for ensuring safe and appropriate content generation, this post focuses on input guardrails.

The Problem

many companies lose valuable time, money, and api tokens processing requests not intended for their systems. this highlights the need for effective input guardrails to prevent:

  • prompt injections
  • harmful or inappropriate prompts
  • requests for illegal content
  • generation of sexually explicit material
  • racist or discriminatory content

The Solution: A Three-Stage Iterative Process

we'll use a smaller, cheaper llm (like a cloud 3 haiku version or gemini 1.5 flash version) for prompt development.

Stage 1: Initial Implementation (v0)

  • create an initial prompt focusing on filtering out undesirable content
  • test this prompt against an evaluation dataset labeled for safety (true/false)

Stage 2: Feedback Analysis (v1)

  • collect and manually label user input data
  • feed this data to the llm alongside the v0 prompt
  • analyze system performance:
    • identify missing aspects of the prompt
    • detect inappropriate queries slipping through
    • evaluate false positives and negatives

Stage 3: Refinement (v2)

  • refine the prompt based on feedback
  • analyze new user input data
  • identify misclassifications
  • create a dataset from these edge cases
  • use this dataset with refined prompt for further llm feedback

Stage 4: Final Optimization (v3/v4)

through this iterative process, the prompt becomes highly refined and effective at preventing inappropriate content, resulting in:

  • significant cost savings
  • reduced processing time
  • improved system safety
  • better user experience

Conclusion

effective input guardrails are crucial for llm safety and efficiency. iterative development through feedback analysis is key to creating robust and accurate prompts, leading to a considerable improvement in the system. remember: each product will need its own unique guardrails tailored to its specific use case and requirements.

Resources

for practical implementation examples and detailed guidance on implementing guardrails, check out these resources: