Layer Legend
Gyaan — the concept
Use case — proof in the wild
Hands-on — you do it
Session Modules
1
Why the SDLC never stays still — and what AI changes now
Introduction
⏱ 30 min
Gyaan
The SDLC as a decision chain, not a process
Every phase of the SDLC is a sequence of decisions under uncertainty. AI doesn't replace the process — it compresses the uncertainty at each gate. Covers the shift from AI-as-tool to AI-as-co-pilot across planning, build and ops.
Use case
From 6-week discovery to 8-day sprint zero
An IT services firm wins a digital transformation engagement. Using AI across requirement synthesis, feasibility scoping and stakeholder alignment, they compress discovery from 6 weeks to 8 days — without cutting corners. Walkthrough of what changed and what didn't.
2
Brief to backlog — AI in discovery, requirements and planning
Plan
⏱ 45 min
Use case
The 40-page RFP that became a sprint-ready backlog in 4 hours
A BA receives a complex client RFP on Monday morning. Using AI-assisted extraction and decomposition, they produce a structured epic-story-criteria hierarchy by lunch. Live replay of the prompting approach — anonymised, reproducible.
Gyaan
Prompt engineering for BAs and product owners
Three prompt patterns that matter most in discovery: extraction (pull structure from unstructured input), decomposition (break epics into stories with acceptance criteria), and stress-testing (ask AI to find gaps in your own backlog). No coding required.
Hands-on
Generate a backlog from a problem brief
Each participant receives a 3-sentence client problem statement. Using the prompt patterns just taught, they generate 5 user stories with acceptance criteria — then AI stress-tests their own backlog for gaps. Business and tech participants do this side by side.
3
Build faster, break less — AI in development and QA
Build
⏱ 40 min
Gyaan
The 40% developer tax — and how AI reclaims it
Developers spend nearly 40% of their time on tasks AI can assist with: boilerplate, documentation, test writing, code review and context-switching overhead. Maps those tasks to specific AI interventions — grounded in what's available today, not what's coming.
Use case
QA cycle: 2 weeks to 3 days on a live product sprint
A product company integrates AI test case generation and auto-regression into a sprint. The use case covers what the QA lead changed, what they didn't trust to AI, and what the delivery manager saw on the dashboard. Two perspectives, one story.
— 15-minute break —
4
Ship with confidence — AI in deployment and incident response
Ship
⏱ 45 min
Gyaan
The last mile problem — why deployments fail and how AI changes the odds
Most production failures aren't code failures — they're coordination failures. AI's highest-leverage point in the SDLC is often the 48 hours around a release. Covers AI's role in pre-release validation, go/no-go logic and post-release monitoring.
Use case
The 2am incident that resolved itself before the team woke up
A 12-microservice release triggers a cascade failure at 2am. AI observability tooling identifies root cause, ranks probable fixes, auto-generates the postmortem and pings the right team member — all within 11 minutes. Walkthrough of the timeline, tool stack and decision log.
Hands-on
Triage a staged incident using AI
Participants receive a simulated error log dump. Using a guided prompt template, they ask AI to summarise the failure, identify probable cause and draft a 3-line client communication. Business participants focus on the communication; tech participants validate the diagnosis.
5
From Workshop to Workplace — your AI-SDLC Play
Champion
⏱ 30 min
Use case
How one team went from curious to committed in 30 days
An IT services delivery team leaves a similar workshop with three commitments. Thirty days later: one is in production, one stalled at procurement, one became a client proposal. Honest case — what worked, what hit a wall, and what the PM wishes they had done on day one.
Gyaan
The AI champion's toolkit — influence, not just knowledge
Being an AI champion is a political and social challenge as much as a technical one. Covers three stakeholder conversations that matter most: your team lead, your client, and your own resistance. Includes the "client won't allow AI" reframe used in regulated accounts.
Key Takeaways
A clear framework for where and how AI fits across every SDLC phase
Exposure to 5 real-world use cases — from discovery compression to incident auto-triage
Hands-on experience with prompt patterns for BAs, developers, QA leads and delivery managers
Familiarity with AI tooling matched to SDLC phase and team role
Prompt Library — 9 ready-to-use templates across SDLC phasesDeliverable