How to Make More Reliable Reports Using AI – A Technical Guide

Over the past year, I've gained valuable insights into creating reliable reports using AI while running my own AI software development and consulting agency. Working with clients from various fields, including digital marketing, SaaS, and cybersecurity, I've learned about the common challenges, mistakes, and best practices in AI software development. In this technical guide, I will share my experience and provide tips on how to make AI-generated reports more reliable and practical.

1. Quick Fixes

Use Markdown: Format tables in markdown for better LLM (Large Language Model) understanding.
Write Clear Prompts: Avoid confusion by providing clear, concise instructions.

2. Optimize Models

Choose the right model for the task: Check LLM leaderboards to select the most suitable model.
Adjust settings: Modify parameters like max tokens and temperature for better performance.
Consider using long-context models: Use these models for tasks that require detailed reports.

3. Smart Prompting

Add phrases like “Explain step by step”: This improves accuracy using Chain-of-Thought prompting.
Include a few-shot examples: Provide examples to guide LLM responses.

4. Choose the Right Framework

Avoid overly complex frameworks: Opt for lightweight options like DSPy, designed with evaluation in mind.

5. Evaluate and Iterate

Use evaluation pipelines: Test and optimize prompts, inputs, and outputs to improve the system.
Measure LLM program outputs against expected outputs: Refine instructions based on evaluation results.

6. Simplify Your System

Minimize API calls: Reducing the number of API calls enhances reliability.
Streamline components: Simplify and optimize workflows for efficiency.

By following these guidelines, you can maximize the reliability of AI-generated reports. Remember, simplicity is key, and constantly reviewing and optimizing your system will lead to better results.

This checklist is not exhaustive and is based on the experience of FireBird Technologies. Your feedback and suggestions for improvement are welcome. Feel free to reach out for help in simplifying your AI pipeline.

Thank you for reading!

Author: Arslan Shahid