GenAI QA Transformation Roadmap
Bharat VarshneyAmbassador
1d ago 12 0
6-Month QA → GenAI QA Transformation Roadmap
💎Month 1:Objective: Shift from test execution to system validation thinking.
Learn:
- LLMs: tokens, embeddings, temperature, determinism vs variability
- Why traditional testing breaks for GenAI
- Core GenAI failure modes: hallucination, bias & unsafe output, prompt sensitivity, latency & cost instability
Hands-on:
- Build a simple LLM prompt-response evaluator
- Compare fixed vs variable outputs across temperature changes
- Log prompts, responses, metadata
Tools:
- OpenAI/gemini api free
- Python + basic prompt experiments
💎Month 2: LLM Evaluation & Metrics (Core QA Skill Upgrade)
Objective: Learn how GenAI quality is measured.
Learn:
- Evaluation dimensions: correctness, faithfulness, relevance, context recall, ground truth vs reference-free evaluation, accuracy vs usefulness in GenAI
Hands-on:
- Build automated evaluation pipelines
- Run batch evaluations on prompt variations
- Compare model versions objectively
Tools:
- RAGAS (RAG + context evaluation)
- DeepEval (unit-style LLM tests)
- Braintrust (dataset-driven evals)
Deliverable:
- LLM evaluation report with metrics & failure classification
💎Month 3: RAG & Knowledge Reliability Testing
Objective: Validate AI systems backed by enterprise data.
Learn:
- RAG (RAG + context evaluation)
- DeepEval (unit-style LLM tests)
- Braintrust (dataset-driven evals)
• RAG architecture failure points: bad chunking, embedding mismatch, retrieval drift.
• Why hallucinations often come from retrieval, not models.
Hands-on: test retrieval precision & recall, inject corrupted documents, validate answer faithfulness to sources.
QA now validates data pipelines, not just application logic.
💎Month 4: Observability, Tracing & Production Readiness.
Objective: Make GenAI debuggable in production.
Learn: logs ≠ traces for LLMs, prompt lineage & versioning, model behavior drift detection.
Hands-on: trace prompt → tool → response chains, detect latency spikes & token explosions, compare behavior across deployments.
Tools: LangSmith (tracing & debugging), Arize (drift & monitoring).
Deliverable: production-ready GenAI observability dashboard.
💎Month 5: Safety, Guardrails & Risk-Based AI Testing.
Objective: Prevent enterprise-level AI failures.
Learn: AI risk categories: data leakage, unsafe instructions, compliance violations, prompt fixes vs. system controls.
Hands-on: build red-team prompt suites, validate refusal behavior, test boundary violations.
Tools: Guardrails AI, custom policy-as-code checks.
💎Month 6:
Enterprise reality: legal, security, and QA intersect.
Objective: Test AI systems that plan and act.
Learn:Agent architectures (planner, executor, memory)
- Non-deterministic workflows
- Why step-based test cases fail
Hands-on:
- Test multi-step agents
- Validate:
- Goal completion rate
- Unsafe action rate
- Recovery from failure
- Introduce human-in-the-loop gates
Comment “AI” if you need a PDF for roadmap
#SDET #GenAI #bharatpost
💎Month 1:Objective: Shift from test execution to system validation thinking.
Learn:
- LLMs: tokens, embeddings, temperature, determinism vs variability
- Why traditional testing breaks for GenAI
- Core GenAI failure modes: hallucination, bias & unsafe output, prompt sensitivity, latency & cost instability
Hands-on:
- Build a simple LLM prompt-response evaluator
- Compare fixed vs variable outputs across temperature changes
- Log prompts, responses, metadata
Tools:
- OpenAI/gemini api free
- Python + basic prompt experiments
💎Month 2: LLM Evaluation & Metrics (Core QA Skill Upgrade)
Objective: Learn how GenAI quality is measured.
Learn:
- Evaluation dimensions: correctness, faithfulness, relevance, context recall, ground truth vs reference-free evaluation, accuracy vs usefulness in GenAI
Hands-on:
- Build automated evaluation pipelines
- Run batch evaluations on prompt variations
- Compare model versions objectively
Tools:
- RAGAS (RAG + context evaluation)
- DeepEval (unit-style LLM tests)
- Braintrust (dataset-driven evals)
Deliverable:
- LLM evaluation report with metrics & failure classification
💎Month 3: RAG & Knowledge Reliability Testing
Objective: Validate AI systems backed by enterprise data.
Learn:
- RAG (RAG + context evaluation)
- DeepEval (unit-style LLM tests)
- Braintrust (dataset-driven evals)
• RAG architecture failure points: bad chunking, embedding mismatch, retrieval drift.
• Why hallucinations often come from retrieval, not models.
Hands-on: test retrieval precision & recall, inject corrupted documents, validate answer faithfulness to sources.
QA now validates data pipelines, not just application logic.
💎Month 4: Observability, Tracing & Production Readiness.
Objective: Make GenAI debuggable in production.
Learn: logs ≠ traces for LLMs, prompt lineage & versioning, model behavior drift detection.
Hands-on: trace prompt → tool → response chains, detect latency spikes & token explosions, compare behavior across deployments.
Tools: LangSmith (tracing & debugging), Arize (drift & monitoring).
Deliverable: production-ready GenAI observability dashboard.
💎Month 5: Safety, Guardrails & Risk-Based AI Testing.
Objective: Prevent enterprise-level AI failures.
Learn: AI risk categories: data leakage, unsafe instructions, compliance violations, prompt fixes vs. system controls.
Hands-on: build red-team prompt suites, validate refusal behavior, test boundary violations.
Tools: Guardrails AI, custom policy-as-code checks.
💎Month 6:
Enterprise reality: legal, security, and QA intersect.
Objective: Test AI systems that plan and act.
Learn:Agent architectures (planner, executor, memory)
- Non-deterministic workflows
- Why step-based test cases fail
Hands-on:
- Test multi-step agents
- Validate:
- Goal completion rate
- Unsafe action rate
- Recovery from failure
- Introduce human-in-the-loop gates
Comment “AI” if you need a PDF for roadmap
#SDET #GenAI #bharatpost