Follow-Up Building the Multi-Model Prompt Evaluation Framework

Video created by Sora

AI helped shape the words here, but the ideas, experiments, and code are 100% human-made. This is part 1 of a series on prompt engineering—turning intuition into engineering.

A Note on Format: While this information is dense, I’ve worked out a new formatting approach for this post to break down the overall idea into the easiest way to absorb it. I’m also limited by the formatting options within LinkedIn’s posting system.

Starting with my next post, I’ll be posting an abbreviated version here on LinkedIn and the full post on a platform that allows me to visually show the posts as I have envisioned them.

In my last post, I shared early findings from testing over 10,000 prompts (including variations and iterations) across seven major models: GPT-4, Claude, Grok, Gemini, DeepSeek, Mistral, and LLaMA. That gave the big-picture overview.

Now, the real work begins. Over the next several weeks, I’ll unpack the evaluation framework I built to understand, improve, and scale prompt performance across wildly different LLM architectures. This series is for developers, researchers, and AI builders tired of fragile prompts, let’s turn intuition into engineering.

Image created by DALL-E

The Challenge

The explosion of large language models has exposed a massive blind spot: How do we create prompts that stay reliable across architectures? Prompt engineering feels like an art, intuitive tweaks here, clever phrasing there. But most approaches lack rigor. No scalable testing. No repeatability. What shines in GPT-4 might crash in Grok or LLaMA.

The fallout? A chasm between one-off wins and ecosystem-wide trust. Too many prompts break unpredictably.

We’re building on sand.

My framework aims to fix that, bridging the gap with structured, auditable methods.

What’s Coming Next

Buckle up—this series will deliver actionable insights:

• Prompt Typologies: The full categories I tested, from zero-shot and few-shot to ReAct and Tree-of-Thought. (and many more)
• Controlled Testing Methodology: How I ensured fair, comparable outputs across models and setups.
• Quantitative and Qualitative Metrics: Measuring token efficiency, output diversity, format compliance, and beyond.
• Failure Patterns: Where prompts break consistently, and why it happens.
• Verification Workflows: Systems to block placeholders, enforce schemas, and guarantee reproducibility.

Each post will include real examples, code snippets, and lessons learned to help you apply this in your own work.

Why This Framework?

I designed this framework with two laser-focused goals:

• Benchmarking: A consistent yardstick to compare prompt performance across models, spotting strengths and weaknesses.
• Development Foundation: Tools to craft prompts that are portable, well-documented, and ready for production deployment.

No more starting from scratch every time, build once, adapt everywhere.

Core Principle

Prompts aren’t casual chit-chat. They’re programmatic assets, software components in disguise.

Like any code, they demand:

• Version Control: Track changes and iterations.
• Schema Validation: Enforce output structures (e.g., JSON, YAML).
• Formal Testing: Run against real model behaviors, not just hunches.

This mindset ditches placeholders, shortcuts, and “it worked once” stories. Instead, embrace auditable, repeatable practices that scale.

Initial Scope

Phase one kicked off with 290+ fully baked prompts, these formed the core dataset, expanded through variations to hit 10,000+ tests. Each was mapped to key categories:

• Zero-shot, few-shot, and role-based prompts.
• Chain-of-Thought, ReAct, and Tree-of-Thought structures.

Strict rules applied:

• Format-Aware: Outputs locked to JSON, YAML, or Markdown.
• Structure-Bound: Tailored for instructional, narrative, or multi-turn scenarios.
• Complexity-Scaled: From beginner tasks (e.g., simple summaries) to expert-level (e.g., multi-step reasoning).

Every prompt was production-complete, no placeholders, no half-baked ideas. This ensured real-world applicability from day one.

System Architecture

At the heart is a closed-loop evaluation pipeline: Metadata → Tokenizer Benchmarking → LLM Inference → Output Capture → Downstream Comparison.

Each prompt became a data artifact with:

• Defined type and target task.
• Expected output format.
• Model compatibility tags.

Tests ran under ironclad controls: fixed temperature, top-K, top-P, and token limits. This enabled apples-to-apples comparisons, revealing exactly where generations derailed.

Image created by DALL-E

Storage and Organization

Prompts lived as version-controlled Markdown files with YAML frontmatter—human-readable yet machine-parsable, with full Git history.

I used Obsidian as the hub, tapping its linking and graph views to:

Spot clusters of similar prompt structures. Pinpoint recurring failure patterns. Track evolution over time.

Pairing Obsidian’s local-first setup with Git delivered security, flexibility, and traceability, from rough drafts to polished production assets.

Advanced Handling

Different prompt flavors needed custom tweaks:

Instructional queries: Rigid schema validation. Retrieval-augmented prompts: Model-specific adapters. Tool-based agent calls: Preset configurations.

This kept everything airtight, adapting to the nuances of each type without losing control.

Evaluation Methodology

I blended qualitative and quantitative lenses:

• Qualitative Checks: Format correctness, hallucination resistance, role adherence.
• Quantitative Metrics: Token efficiency, output diversity, time-to-first-token.

The result? A holistic view that goes beyond “does it work?” to “how well, and why?”

Underlying Principle Prompt engineering can’t stay stuck in trial-and-error loops. It must mature into an engineering discipline: structured generation, rigorous testing, comprehensive docs. Without this, prompts remain flaky interfaces, unreliable bridges between humans and AI.

What’s Next We’ll explore where the framework nailed it… and where it stumbled. The next post tackles the biggest early hurdles: mismatches between instructions and outputs, memory leaks in workflows, and the verification gaps that threatened reliability.

Expect deep dives, fixes, and tools you can steal for your projects.

✨ Takeaway: Prompts are software.

They must be designed, versioned, tested, and audited with the same rigor we apply to code.