Technical Research and Prototyping (TRAP)

TRAP Framework: 7-Step Technical Research & Prototyping

Step 1: Problem Definition & Scope Start by clearly articulating the technical challenge or opportunity you're exploring. Define success criteria, constraints, and what questions you need answered before committing to a full project.

• Write a clear problem statement that describes the user pain point or technical gap you're addressing, including who experiences this problem and why it matters
• Define 3-5 specific research questions or hypotheses you need to validate (e.g., "Can AI accurately extract structured data from unstructured documents?")
• Set boundaries: what's in scope vs. out of scope for this TRAP session, including timeline constraints, budget limits, and technical areas you won't explore

Step 2: Landscape Analysis Research the current state of solutions in this space. What existing tools, libraries, frameworks, or platforms address similar problems?

• Map the ecosystem by identifying 8-10 relevant solutions, categorizing them (open source vs. commercial, cloud vs. on-premise, API vs. self-hosted)
• Review recent developments by checking tech blogs, GitHub trending repos, product launches from the last 6-12 months, and relevant AI model releases
• Identify market trends and gaps by noting what problems remain unsolved, where users are complaining, and what emerging approaches are gaining traction

Step 3: Competitive & Alternative Analysis Go deeper into 3-5 specific competitors or alternative approaches. Understand how they solve the problem technically and what users think about them.

• Create detailed profiles for each competitor including their tech stack, architecture approach, key features, pricing model, and target user segment
• Analyze user feedback by reviewing G2/Capterra reviews, GitHub issues, Reddit discussions, and support forums to understand real-world pain points and praise
• Identify differentiation opportunities by noting what competitors do poorly, what features are missing, and where you could provide unique value

Step 4: Technology Stack Evaluation Based on your research, identify candidate technologies, AI models, APIs, or tools you might use for implementation.

• Build a comparison matrix evaluating 3-5 technology options across criteria like cost, performance, ease of integration, documentation quality, community size, and vendor lock-in risk
• Assess AI/ML requirements including which models or APIs are suitable (GPT-4, Claude, Llama, specialized models), expected latency, accuracy needs, and inference costs
• Consider infrastructure needs such as hosting requirements, scalability constraints, security/compliance requirements, and development environment setup complexity

Step 5: Rapid Prototyping Build a minimal proof-of-concept focusing on the riskiest assumptions or most critical technical questions.

• Start with the highest-risk element first, whether that's a complex algorithm, third-party API integration, or novel AI application that might not work as expected
• Keep it minimal by building only what's needed to test your core hypothesis—avoid UI polish, error handling, or edge cases unless they're critical to learning
• Document as you go by taking notes on unexpected challenges, performance observations, and key technical decisions so insights aren't lost

Step 6: Testing & Validation Put your prototype through its paces with realistic scenarios and gather data on what matters.

• Run structured tests using representative data or use cases that mirror real-world conditions, measuring key metrics like accuracy, speed, reliability, and resource usage
• Identify breaking points by pushing the prototype to its limits—what happens with large datasets, edge cases, concurrent users, or malformed inputs?
• Gather stakeholder feedback by sharing the prototype with 2-3 potential users or technical colleagues to understand if it actually solves the problem and what concerns emerge

Step 7: Synthesis & Decision Compile your findings into a clear recommendation with supporting evidence.

• Create an executive summary with a clear go/no-go/pivot recommendation, supported by the most compelling evidence from your research and testing
• Document technical requirements including recommended tech stack, estimated development effort (in weeks or story points), key risks, and dependencies or prerequisites
• Prepare next steps by outlining what a full project would entail, including team composition needs, timeline estimates, open questions requiring further research, and quick wins you could pursue first

This framework gives you a structured path from initial curiosity to informed decision-making, ensuring your TRAP workshops produce actionable insights rather than just interesting research.