A practical framework for using Large Multimodal Models (LMMs) inspired by a real misuse example

This project started after reading a forum thread where someone tried to use an LMM to prove a controversial claim and guided the model step by step toward a predetermined conclusion. The prompts were clever but fundamentally flawed. Instead of using the model to test an idea, they used it to validate bias by using leading prompts, closed feedback loops, and internal logic questions without asking for critical evaluations. That conversation became the spark for a deeper question: how do we keep LMMs honest, verifiable, and useful?

In just a couple of weeks, I collaborated with ChatGPT, DeepSeek, Claude, and Grok to design, test, and refine the Guide to Using Large Multimodal Models (LMMs). Each model contributed differently, helping structure the framework, validate reasoning, and improve clarity. The process itself showed how well LMMs can co-develop a complex framework when guided by clear objectives.

The result is a framework for reliable, auditable, and responsible AI use. It is built to move users from ad hoc prompting to repeatable workflows that can stand up to scrutiny in real world environments.

The guide covers:

Prompt Engineering Patterns from zero shot to structured chaining

Verification and Troubleshooting Loops catching bias and hallucination early

Multimodal Inputs integrating text, image, and data reasoning

Governance and Deployment aligning AI behavior with human oversight

Security and Fine Tuning Frameworks ensuring trustworthy operations

You can find the full guide and six technical supplements on GitHub:
https://github.com/russnida-repo/Guide-to-Large-Multimodal-Models