Sprint Learn Academy

Measure what people can do with AI.

AIQ assesses demonstrated AI capability, not self-reported claims, and turns the evidence into reports that candidates, managers, interviewers, L&D and leadership can act on.

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RAG & Knowledge Systems 6 items
Said Proficient
Showed 52%
Hollow marks are what someone reports. Solid marks are what they demonstrated. Only solid marks count toward AIQ.

How it works

  1. Share your context

    An optional five-minute survey: your role, stack, the AI tools you use and what you have built. It helps choose relevant questions and never changes your score.

  2. Take an assessment that fits

    A fresher gets foundations and a beginner practical task. A senior engineer gets RAG architecture, AI security, evaluation and production design. Everyone at the same level gets an equivalent paper.

  3. Get an evidence report

    Your AIQ, competency scores, practical work, how you work with AI, role readiness and a learning plan, with a credential anyone can verify.

One assessment, a view for everyone

The same evidence, shown to each person with what they need and only what they are allowed to see.

Candidates
Strengths, gaps and a practical learning plan.
Managers
Role readiness and where a conversation would help.
Interviewers
Validation areas, suggested questions and the answer-level evidence.
L&D
Training priorities by competency, role and department, and pre/post change.
Leadership
Heatmaps, AI champions, tool capability and benchmarks, with drilldown to evidence.

What the evidence covers

  • AI & ML FundamentalsHow machine learning and large language models work: training vs inference, tokens, embeddings, probabilistic outputs, model limits.
  • Prompt EngineeringStructuring instructions, context, examples, constraints and output formats to get reliable results from LLMs.
  • Generative AI & LLMsCapabilities and limits of generative models: hallucination, context windows, multimodality, model selection.
  • Responsible AI & GovernanceFairness, transparency, accountability, human oversight, regulation and organisational AI policy.
  • AI for ProductivityUsing AI assistants safely and effectively for everyday knowledge work: drafting, analysis, summarisation, research.
  • Data for AIData quality, preparation, privacy classification and data pipelines that feed AI systems.
  • AI Strategy & ValueIdentifying use cases, building business cases, adoption, change management and measuring AI value.
  • AI-Assisted Software DevelopmentUsing coding assistants for design, implementation, testing and review while keeping code quality and security.
  • LLM Integration & APIsCalling model APIs: messages, parameters, structured output, tool calling, streaming, retries and rate limits.
  • RAG & Knowledge SystemsRetrieval-augmented generation: chunking, embeddings, vector search, reranking, grounding and citation.
  • Agentic AIAgents and workflows that plan, call tools and act: orchestration patterns, guardrails, human-in-the-loop.
  • AI SecurityPrompt injection, data leakage, insecure output handling, supply-chain and access-control risks in AI systems.
  • AI Evaluation & QualityMeasuring AI output quality: test sets, metrics, LLM-as-judge, regression testing and monitoring.
  • Cost & PerformanceToken economics, latency, caching, batching, model routing and capacity planning.
  • Production AI ArchitectureDesigning reliable, observable, scalable AI systems: gateways, fallbacks, versioning, deployment.

Check a credential

Every final result carries a credential ID and QR code. Enter the ID to see what was demonstrated and whether it is still valid.