Weeks 1-2: How AI Accelerated Product Strategy

“LLM Tech Stack Spike Project”—that’s how Mission Control started. It was a clear sign of missing strategy. Over two weeks, dozens of focused AI sessions gave me clarity: a product vision, validated architecture, compelling value—and, most importantly, a replicable process to compress months of product discovery into days.

Here’s how I transformed 50+ hours of conventional research into 15 hours of AI-driven strategy, with greater impact and less guesswork.

Establish a Strategic North Star First

While I knew why I was moving forward with a deep dive exploration into LLM technology, when first getting started I didn't know what I should build. I knew I wanted to truly test the limits of LLMs in real world Product Development scenarios. So I started with a North Star definition of what matters most. In the last blog post we elaborated on what these were:

• We must build on infrastructure that demonstrates strong Performance, Reliability, & Scalability.
  
• We must build on a tech stack that allows for Flexibility & Extensibility.
  
• We need to build Universal, Expected Capabilities that real world Product Development teams build in real world products.
  
• We must use Real-World Development Workflows that would be the expectation of any team today.
  

With this North Star defined, it became the basis for Product Strategy discovery / research prompts that fed into the LLMs assisting me.

The decisions that I needed to make during this phase boiled down to the following:

1. What would make up my technology stack? (architectural design)
   
2. What would be the best demonstration of a real-world product I should build? (product definition)
   
3. How could I best articulate WHAT I was going to build and WHY it matters? (product marketing)
   

To set out answering these questions I knew I needed to establish a strong discovery approach that also helped me maximize what I was getting out of the LLM platforms I wanted to test. Here's ultimately what worked best.

A Product Strategy Framework to Maximize Productivity with LLM Assistance.

Multiple Platform "AB Testing" or "Shootouts"
Relying on a single AI platform invites blind spots. Instead, we did "shootouts" or "AB tested" the major LLM platforms. Early on, we would pass the same prompts and evaluate the quality of response/returns. These were the platforms we AB tested:

• Claude Sonnet 4
  
• ChatGPT (o3/o4)
  
• Perplexity Pro
  
• Microsoft Copilot
  
• ChatPRD (Specialized)

I got into a good rhythm of spending time up front defining good, solid prompts, and then I'd pass the same prompt into multiple platforms / models and compare responses.

Breadth-->Depth Narrowing Approach

My approach for discovery and research typically started with a breadth-first prompt. As I evaluated responses from the LLMs I would choose the best 2-3 platforms and narrow my prompting focus, moving to a more depth first prompting strategy. I would iterate on these prompts and also narrow the field of AI platforms as I went until I ultimately was able to make a final decision.

It wasn't about asking more questions—it was about asking the right ones, at the right times.

Breadth-first: Comprehensive prompt to all platforms to scope the landscape and identify potential leading options, based on requirements, goals, or constraints.

Depth-focused: Drilled deep on the leading AI platform for technical details, use cases, pros and cons, etc on the leading options.

Decision-point: Used final prompts to surface risks, confirm mitigation strategies, and ensure robust decisions.

The Final Decision: Human Verified

Ultimately I had narrowed down to one LLM with a set of recommendations. From here it was important to validate and make the final decision myself. This typically involved my own deep scan of the offering or approach (whatever the decision was) and confirming it felt right. It was in these moments I realized my experience/expertise (and gut) should be the ultimate decider.

LLM Productivity Gains Across Three Major Decision Areas

Leveraging the research->decision framework described above, I was able to put a comprehensive Product Strategy together that included major decisions across the following areas.

1. Technical Foundation Decisions

Goal: Choose a technology stack and architectural approach that would be Performant, Reliable, and Scalable and give me the greatest Flexibility, and Extensibility.

Time: 10 hours (vs. 20-50 hours without LLM assistance)

• Compared stacks and architectures—all platforms, framed to my constraints
  
• Evaluated integration and risks
• AI excelled at trade-off analysis, delivering actionable, context-specific recommendations (e.g., cross-platform, real-time, and scalability considerations)
  

Result: Significant: 2-5x faster research, deeper analysis, catered to my North Star goals.

2. Product Definition

Goal: Determine the best Product Definition (vision, features, phased plan) that aligns with Universal, Expected Capabilities that modern Product Development teams (or technical founders) would expect to build.

Time: 4 hours (vs. 6-10 hours without LLM assistance)

• Real-time market research via Perplexity Pro
  
• Product requirements and feature prioritization with ChatPRD
  
• Cross-AI value proposition testing
  
• Persona validation through iterative prompts
  

Result: Solid: 1.5-2x efficiency gain, stronger strategic clarity

3. Value Prop / Positioning

Goal: Articulate the value and positioning effectively as a product marketer would develop.

Time: 2 hours (vs. 3-5 hrs without LLM assistance)

• Value proposition and elevator pitch brainstorming/ideation
  
• Hypothetical marketing messaging to target audiences explorations
  
• Audience segmentation aligned for each stakeholder group
  

Result: Solid: 1.5-2x efficiency gain, broader creative messaging to select from

What Resulted and What This Means for You

By leveraging systematic, AI-powered research and decision-making, we delivered the foundation for a B2B SaaS application focused on team collaboration and communication—Mission Control. The tech stack we arrived at includes GCP/Firebase, Flutter, Hasura, and Directus CMS, all validated for scalability and efficiency.

Here’s what this means for you: AI doesn’t replace your expertise—it amplifies it. With AI-driven strategic research and trade-off analysis, you gain clarity, speed, and precision that traditional methods can’t match. The result? Faster execution, better decisions, and streamlined validation across platforms.

To achieve these productivity gains, adopt the framework outlined here. Integrating AI into your discovery and early decision-making processes can unlock significant advantages for your team—without sacrificing rigor or quality.

 
Next up: Post 4 will dive into our experiences leveraging AI to develop Product Designs.