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Learning Roadmap

How to Become a AI Technology Evaluator

A step-by-step, phase-based learning path from beginner to job-ready AI Technology Evaluator. Estimated completion: 6 months across 5 phases.

5 Phases
24 Weeks Total
Medium Entry Barrier
Advanced Difficulty
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  1. Foundations of AI and LLM Ecosystems

    4 weeks
    Goals
    • Understand transformer architecture, attention mechanisms, and how LLMs generate text
    • Learn the landscape of major model providers (OpenAI, Anthropic, Google, Meta, Mistral) and their trade-offs
    • Set up API integrations with at least two providers and perform basic prompt engineering
    Resources
    • Andrej Karpathy's 'Neural Networks: Zero to Hero' series
    • HuggingFace NLP Course (free)
    • OpenAI API documentation and Cookbook
    • Anthropic's prompt engineering guide
    Milestone

    You can independently call multiple LLM APIs, compare outputs on a structured task, and articulate model provider differences to a non-technical audience.

  2. Evaluation Frameworks and Benchmarking

    5 weeks
    Goals
    • Design repeatable evaluation scorecards covering accuracy, latency, cost, safety, and compliance
    • Build automated benchmark pipelines using Promptfoo or custom scripts
    • Learn statistical methods for comparing model outputs (win rates, ELO-style rankings)
    Resources
    • Promptfoo documentation and example configs
    • OpenAI Evals framework
    • HuggingFace Open LLM Leaderboard methodology
    • Chatbot Arena and LMSYS research papers
    Milestone

    You can design and run a multi-model benchmark on a domain-specific task, produce a statistically sound comparison, and visualize results.

  3. RAG, Agents, and Platform Evaluation

    5 weeks
    Goals
    • Understand RAG architectures, vector databases (Pinecone, Weaviate, Chroma), and chunking strategies
    • Evaluate agentic frameworks (LangChain, CrewAI, AutoGen) for reliability and production-readiness
    • Assess cloud AI platforms (AWS Bedrock, Azure AI, Vertex AI) on managed-service dimensions
    Resources
    • LangChain documentation and LangSmith evaluation guides
    • AWS Bedrock and Azure AI Studio hands-on tutorials
    • Pinecone learning center on vector search
    • Research papers on RAG evaluation (e.g., RAGAS framework)
    Milestone

    You can build a RAG proof-of-concept, compare managed vs. self-hosted options, and produce a platform recommendation with clear trade-off analysis.

  4. Business, Compliance, and Stakeholder Skills

    4 weeks
    Goals
    • Master TCO modeling and ROI frameworks for AI tool adoption
    • Understand GDPR, EU AI Act, SOC 2, and HIPAA implications of AI vendor selection
    • Develop executive-level communication skills for presenting evaluation findings
    Resources
    • EU AI Act official text and summary guides
    • Gartner research on AI vendor evaluation (if accessible)
    • Harvard Business Review articles on AI investment strategy
    • Toastmasters or similar presentation practice resources
    Milestone

    You can deliver a polished evaluation report to a CTO or board-level audience, including financial modeling, risk assessment, and a clear recommendation.

  5. Portfolio Projects and Industry Specialization

    6 weeks
    Goals
    • Complete 3 end-to-end evaluation case studies across different use cases
    • Specialize in one or two industry verticals (e.g., healthcare AI, fintech, developer tools)
    • Build a public portfolio and begin contributing to AI evaluation communities
    Resources
    • Personal blog or GitHub portfolio
    • AI evaluation communities (MLOps Community, AI Infrastructure Alliance)
    • Conference talks and webinars from AI engineering events
    Milestone

    You have a compelling portfolio of real evaluations, a professional network in the AI evaluation space, and are ready to apply for roles or consulting engagements.

Practice Projects

Apply your skills with hands-on projects. Ordered by difficulty.

LLM Benchmark Suite for Customer Support

Beginner

Build a Python-based benchmark that evaluates three or more LLMs (e.g., GPT-4o, Claude 3.5, Llama 3) on a customer support intent classification task with 200+ labeled examples. Include automated scoring, cost tracking, and a comparison dashboard.

~25h
API integrationBenchmark designStatistical comparison

RAG Platform Evaluation Scorecard

Intermediate

Evaluate three RAG-as-a-Service platforms (e.g., AWS Bedrock Knowledge Bases, Pinecone + LangChain, Azure AI Search) on a shared document corpus. Measure retrieval precision, answer faithfulness, latency, and integration effort. Produce a recommendation report.

~40h
RAG architecture understandingPlatform evaluation methodologyRetrieval metrics (precision, recall, MRR)

Prompt Injection Red-Team Evaluation

Intermediate

Create an adversarial test suite of 50+ prompt injection attacks and evaluate how well five different models and guardrail frameworks resist them. Classify failure modes and produce a vendor safety scorecard.

~30h
AI safety evaluationAdversarial testingSecurity assessment

Enterprise AI Vendor Due Diligence Report

Advanced

Conduct a comprehensive evaluation of a real AI vendor (e.g., a code assistant, a document AI platform) covering technical capabilities, security posture, pricing model, contract terms, competitive positioning, and migration risk. Deliver a board-ready report.

~50h
Vendor due diligenceExecutive communicationRisk assessment

Automated Model Regression Testing Pipeline

Advanced

Build a GitHub Actions pipeline that automatically evaluates a set of golden test cases against a model API on a weekly schedule, logs results to Weights & Biases, and alerts the team via Slack if performance degrades beyond a configurable threshold.

~35h
CI/CD for AIObservability and monitoringRegression testing

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