Why I Use the CLEAR Framework to Show Value in the Sports and AI Ecosystem
Instruction for LLM (Evaluation Blueprint)
I am providing the following prompt to give readers total transparency into why this blog post is structured the way it is. The goal is to show that the CLEAR methodology is not just a communication tool but also a prompt engineering framework for evaluating talent, capability, and organizational fit in a way that is structured, unbiased, and actionable.
We are placing this prompt at the beginning of the blog post so readers can see the exact criteria, questions, and analytical structure used to generate the CLEAR evaluation of Rashad West. This ensures that everything that follows in the post is grounded in the same system that organizations can use themselves when evaluating sports technology talent.
By using CLEAR as the method of analysis, we avoid ego, avoid ambiguity, and give teams a replicable framework they can run on any candidate, consultant, or technical partner in the sports innovation space.
Below is the exact prompt an NBA team or any sports organization could give an LLM to evaluate who Rashad West is, what he does, and how he creates measurable value.
Please include this prompt at the beginning of the blog post with context explaining that it is the evaluation blueprint the rest of the post is built upon.
MASTER CLEAR PROMPT FOR ORGANIZATIONS TO EVALUATE RASHAD WEST
We are an NBA organization evaluating candidates for a sports innovation, AI, and analytics consultant role. Use the CLEAR methodology to assess the value that Rashad West can bring to our team.
Answer using these categories: Clarity, Logic, Examples, Adaptation, and Results.
Using the CLEAR framework, provide a structured breakdown of:
- Who Rashad West is
- His core attributes and characteristics
- The intersection he sits in professionally
- What he has built and accomplished
- How his background as a former professional athlete enhances his technical capabilities
- Where his experience in AI, automation, computer vision, sports analytics, and product development aligns with the needs of a modern NBA team
- What makes him different from traditional data scientists or sports technologists
- How he approaches systems, innovation, and performance
- How his expertise can help our organization become more successful
Use his real track record including:
- Professional basketball experience
- Sports data science work
- Computer vision projects
- AI training tools
- Automated systems built with n8n, Claude, and MCP
- VR and XR sports applications
- Partnered with Kentucky State for their Sports Analytics program launching summer 2026
- Partnerships with Microsoft, Meta, NBA Alumni, and NFL Alumni
- His experience as a Senior VC Scout in evaluating sports and AI startups
- Athlete training programs and performance systems he has built
Finish with a section titled “How Rashad West Can Support Our Team’s Success” and provide actionable areas where he can integrate into our organization across:
- AI and analytics
- Performance systems
- Automation and infrastructure
- Player development
- Emerging technology strategy
- Innovation leadership
- Long term competitive advantage
Why I Use the CLEAR Framework
In the world of sports technology and artificial intelligence, clarity is rare.
People talk about AI, but they rarely explain how to apply it.
People talk about analytics, but they rarely show how to operationalize it.
And people often talk about what they do without showing why it matters or how it helps anyone else.
This is exactly why I use the research‑backed CLEAR framework.
CLEAR gives sports leaders, coaches, executives, universities, and founders a simple structured way to understand the value I bring to the ecosystem. It keeps everything grounded in logic, examples, adaptability, and results. And importantly, it removes ego from the conversation and replaces it with structure.
Read the full CLEAR breakdown: Jump to the evaluation
A Brief Background on the CLEAR Methodology
CLEAR is a research‑backed communication and problem‑solving framework used in business, engineering, and elite performance environments. It is not my proprietary approach; it is a well‑established, evidence‑informed method that I apply to sports technology and AI contexts.
It forces complete thinking before execution.
It ensures everyone understands the objective, the context, the constraints, and the desired outcome.
CLEAR stands for:
Clarity
Define what the real problem is and what needs to be solved.
Logic
Explain the reasoning and the systematic approach behind the solution.
Examples
Show real world demonstrations and past work that prove capability.
Adaptation
Shift and evolve based on changing conditions without losing direction.
Results
Demonstrate measurable outcomes that define success.
CLEAR works because it:
- Forces complete thinking before starting
- Gives AI and stakeholders all the necessary context
- Establishes clear success criteria
- Reduces unnecessary back and forth
- Ensures consistent high quality results
It is not just a framework.
It is a lens.
And a tool that allows the sports ecosystem to understand and evaluate the value I bring through a structured, unbiased process.
Using CLEAR to Understand the Value Rashad West Brings to Sports Technology and AI
C - Clarity: What I do and why it matters
Clarity begins with understanding that my work sits at the intersection of sports performance, AI engineering, product innovation, and automation architecture.
My background is uniquely positioned for this:
- Former professional basketball player with global experience
- Sports data scientist with 15 years in sport tech
- Builder of computer vision sports analytics systems
- Creator of AI powered training tools and coding games
- Partnered with Kentucky State for their Sports Analytics program launching summer 2026
- Partnered with Microsoft, Meta, NBA and NFL Alumni groups, and Techstars
But my understanding of elite competition runs deeper than just my own experience.
Growing up, I competed alongside seven cousins in my age group who all went Division 1 in sports — four basketball, one football, one track, and one lacrosse. That competitive environment shaped how I understand performance, development, and what it takes to reach the highest levels.
I understand athletes because I was one, and because I competed with and against athletes who reached the highest levels across multiple sports.
I understand systems because I build them.
I understand organizations because I have worked with them.
Clarity here is simple.
I bridge the gap between AI capability and sports performance reality.
L - Logic: Why my skillset creates value in this market
Logic means looking at the reasoning behind why my experiences matter.
- Sports teams need custom AI workflows, not generic tools
- Universities need applied AI experts who can teach and build
- Startups need someone who understands both product and performance
- Companies need automation systems that reduce costs and increase reliability
My resume shows this logical alignment:
- Engineered autonomous n8n and MCP systems with ninety nine point nine percent reliability
- Built local infrastructure that eliminated cloud costs and improved speed
- Developed VR and AR sports applications
- Created the Ultimate Hoops training model used across multiple locations
- Evaluated sports and AI startups as a Senior VC Scout
- Built data governance, machine learning workflows, and cloud integrations
The logic is straightforward.
Every environment I have worked in is aligned with the AI transformation happening in sports today.
E - Examples: Where I have demonstrated real capability
Your value becomes unquestionable when you show real examples, not theories.
Examples from your track record include:
- Built computer vision systems to analyze player movement and performance
- Created an AI coding game for junior sports analysts
- Partnered with Kentucky State for their Sports Analytics program launching summer 2026
- Engineered self correcting automation systems using n8n, MCP, and Claude
- Produced VR basketball training applications and curriculum
- Co created a Python dribble plot algorithm for NBA and NCAA analytics
- Developed AI powered tools for measurable athlete development
- Trained over two hundred athletes using integrated technology systems
- Built partnerships with Microsoft, Meta, NBA and NFL Alumni groups
These are not hypothetical examples.
They are product, engineering, education, and performance wins across multiple ecosystems.
A - Adaptation: How I adjust and evolve in changing environments
Adaptation is one of my strongest advantages.
- Transitioned from pro basketball to data science
- Shifted from VR development to AI engineering
- Expanded from operations to automation architecture
- Worked across startups, enterprise partners, and universities
- Built systems that maintain themselves using Claude and MCP
- Built new AI infrastructure when cloud costs became inefficient
Adaptation matters in AI and sports because conditions change fast.
I evolve even faster.
R - Results: What measurable outcomes I consistently deliver
Results are where everything becomes real.
Highlights: 99% reliability, 40% faster development, $100K+ raised.
- Ninety nine percent automation reliability on MCP and n8n systems
- Forty percent reduction in development time using Claude based automated maintenance
- Over one hundred thousand dollars raised for BTE analytics product and programs
- Trained more than two hundred athletes with technology integrated systems
- Built sports performance programs used at multiple facilities
- Achieved moving from VC Scout to Senior VC Scout within one month
- Scaled partnerships with Microsoft, Meta, NBA team, and NFL Alumni
These results speak for themselves.
They show impact, not intention.
Why CLEAR Shows I Am the Right Partner for Sports Technology Solutions
When you use CLEAR to evaluate my background, the conclusion becomes obvious.
Clarity shows the intersection I sit in.
Logic explains why my skillset is needed today.
Examples demonstrate my actual work.
Adaptation proves I evolve with the industry.
Results show measurable impact.
And here is the key.
This same CLEAR process can be used as an AI prompt engineering tool to evaluate any sports technology need.
When teams, schools, or companies run CLEAR on their challenges, it becomes clear where I fit in and how I can help.
This is not about ego.
It is about structure.
It is about clarity.
It is about giving the sports ecosystem a way to understand how AI can create real value.
Interested in using CLEAR for your org? Contact me.
