Go from calling an LLM to owning the production AI stack.
One hands-on day. Build a working reference system across nine pipelines and walk out able to design, debug, and lead production AI work.
09 Pipelines Covered · 1 Reference System Shipped · 0 Toy Demos
Every stage below is something you'll stand up yourself. The numbered stops map straight onto the nine workshop modules, so by the last session, this is a system you actually own, not a slide you saw once.
Every workflow starts here: the systems, documents, and events already running your business.
Three raw input types, one ingestion pipeline turns them all into structured signal.
Turn systems, documents, and events into clean, structured input the rest of the stack can trust.
Event-driven updates that keep the knowledge base current as source systems change.
What you will build
Retrieval you can trust, cite, and measure.
Build this live. Reserve a seat →Vector, graph, and keyword search fused into one hybrid pass, then reranked before anything moves downstream.
Ranking, compression, and formatting tuned to the model you're actually calling.
One stable interface routing, rate-limiting, and failing over across models and providers.
What you will build
Agents that survive production, not demos.
Build this live. Reserve a seat →Quality loop: every output is drafted, critiqued, and scored against a rubric before it reaches the user.
Calling a chat API is an afternoon's work. Running that same system against real company data, at production traffic, without leaking context or silently degrading, that's a different job, and it's the one companies are actually hiring for.
One notebook, one document, one clean answer. Works every time because nothing ever changes.
Ten data sources updating on ten different schedules, conflicting answers, and a retrieval layer that has to know what it doesn't know.
A single call to a single model, judged by whether the output "looks right."
A gateway routing across models and providers, an evaluation pipeline scoring every change, and a trace for every request when something breaks at 2 AM.
Each outcome maps to a module you build yourself, and to what teams expect from a Senior AI Engineer.
Choose between generate, retrieve, and fine-tune, then design chunking, hybrid retrieval, and reranking with reasons you can defend.
Build representative test sets, measure task success, and turn every failure into a new test case.
Route, cache, rate-limit, and fail over across models so your app never depends on one provider.
Follow a bad answer through retrieval, context, and tool calls to the exact step that broke it.
Give agents tools with guardrails that decide what they may do on their own.
Track tokens, latency, and model spend per request, and make the trade-offs a lead is expected to make.
Decompose hard tasks and connect prompts so each output feeds the next, with review passes that catch errors between steps.
Use few-shot examples, grounding, and rubric-based checks so wrong answers are caught before users see them.
You have 3+ years as a backend or full-stack engineer, have called an LLM API, and want to own production AI work instead of assisting on it.
You are new to programming or want a prompt-writing tutorial. This is a build-the-system day, not an intro.
Nine production pipelines and the prompt-level techniques inside them, across seven phases. Raw enterprise data in the morning, a governed, observable, evaluated AI system live by the end of the day.
The decision framework before you write a line of pipeline code: when a prompt alone is enough, when you need retrieval to ground answers in your own data, and when fine-tuning is actually worth its cost instead of a reflex.
Build and clean a training set from real traffic, label it, and test whether fine-tuning actually beats better retrieval and prompting before you pay for it.
Turn systems, documents, and events into clean, structured input the rest of the stack can trust: chunking strategy (size, overlap, structure-aware), normalization, and metadata design that decides everything downstream.
Keep the knowledge base current as source systems change, without a full reprocessing run every time something upstream moves.
Model relationships between entities so answers reflect how the business actually connects things, not just what happens to be textually similar.
Pick embedding models and dimensions, tune index type and parameters (HNSW, IVF), use metadata filtering and quantization, and measure recall against latency and cost instead of accepting defaults.
Combine vector similarity, keyword search, and metadata filters, rewrite queries, then rerank and tune the candidates so the right passage surfaces instead of just the most similar-sounding one.
Assemble the right context window: ranking, compression, and formatting tuned to the model you're actually calling, with every answer citing the exact source passage it came from.
Curate examples that steer the model, ground answers in retrieved sources, verify citations, and teach the system to say "I don't know" instead of inventing an answer.
Split a complex task into small steps a model can do reliably, instead of one giant prompt that fails in ways you can't locate.
Connect multiple prompts so each output feeds the next, with validation between steps so one bad link doesn't poison the chain.
Draft, critique, and revise in separate passes so errors get caught by a reviewer prompt before they reach a user.
Route, rate-limit, cache, and fail over across models and providers behind a single stable interface your application never has to know changed.
Give the system hands: tool calls, multi-step plans, and the guardrails that decide what it's actually allowed to do on its own.
Prompt caching and semantic caching to cut cost and latency, with the invalidation rules that keep cached answers from going stale.
Trace every request, retrieval hit, and tool call, with latency tracked at each step, so you can debug a system you didn't personally watch run, the difference between guessing and knowing.
Build representative test sets from real queries, write evaluation rubrics that score what matters, measure task success instead of surface-level answer similarity, then review and label failures so they feed back in as new test cases.
Apply the cross-cutting layer that runs under everything: access control, PII handling, and per-model cost tracking wired in from the start.
Chain all nine pipelines, ingestion through evaluation, into one working system: enterprise data in, a governed, observable AI application out.
Built from years architecting data systems. Keynotes and workshops, in-person or virtual, that leave you informed, capable, changed. Over 1,000 professionals across past workshops walked out doing things differently.
Ten capability domains. One production AI platform, built end to end with real enterprise data. Built live, in one day.
Design the full stack end to end with AI-assisted trade-off analysis: when to generate, retrieve, or fine-tune, and how ingestion, retrieval, gateway, and agent layers reason together, not bolted on.
Design ingestion from systems, documents, and events with AI-assisted tooling: chunking strategy, metadata design, and update triggers, directed instead of hand-wired.
Direct AI to tune vector, graph, and keyword retrieval together, then apply reranking, at a depth and speed manual relevance tuning can't match.
Build entity and relationship graphs that ground retrieval in structure, not just semantic proximity, with consistency across the knowledge layer.
Architect what actually reaches the model: ordering, compression, and budget decisions that separate a reliable answer from a hallucinated one.
Embed access control, PII handling, and action guardrails into the stack's design, not as an afterthought, but as architecture.
Trace requests end to end and build automated evals against representative test sets, so regressions get caught and labeled before they ship, not after a user complaint.
Route, cap, and track spend per model and per pipeline through the AI gateway, so cost stays visible instead of surprising you at month end.
Few-shot prompting, decomposition, prompt chaining, and multi-pass review, composed into workflows that stay reliable as tasks get harder.
Ground, verify, and refuse when unsure, then cache prompts and answers so the reliable path is also the cheap and fast one.
One day separates the engineers who ship production AI systems from the ones still wiring an API call to a prompt.
Your org has an AI initiative that needs to move past the demo. You're the one who can wire ingestion through evaluation into something that holds up in production, not just a notebook.
Product needs a working RAG system by Friday? You ship Thursday. Agent pipeline due next quarter? Architecture's ready next week. The person who makes the deadline leads the next one.
Most engineers have opinions about AI. You have artifacts: a working retrieval pipeline, a knowledge graph, an observability dashboard, a physical handbook. "I know how to do this" versus "here is how I did it."
You're not working longer. You're working with a full stack others are still assembling piece by piece. Your team notices. They start asking what changed.
40 seats.
One cohort. Chennai.
Our last data architecture cohort's early bird filled in 4 days. Everyone in this room is building the same nine pipelines you're expected to own within the next year, whichever side of that curve you're on before it becomes the baseline instead of the edge.
Early Bird ₹2,999 ₹3,999, only 10 seats. Miss it and you pay Standard ₹3,999. Cohort closes at 40.
This is not an introduction to prompting. It is a precision workshop for senior professionals who already know how enterprise systems work and want the full production AI stack, with AI directing the heavy lifting. Junior engineers are not the audience.
The AI stack is the leverage skill your systems experience is missing. AI-assisted engineering compresses years of learning retrieval and agent design into one day.
Who make cross-team technical decisions and need AI platform trade-off analysis, retrieval, gateway, agents, in the same toolkit as their software judgment.
AI is the new leverage tool for the platform design work you already do. Move from a single API call to a full production stack in hours, not weeks.
Who build and operate pipelines and want to move from implementation to full architectural ownership of the retrieval and knowledge layer.
Who evaluate platform decisions across software and AI, and want production AI fluency to move as fast as their software judgment already does.
Who lead engineering teams and want AI stack literacy to raise their team's platform decisions, not just their code.
Real feedback from a past Developer Road Ahead workshop.
Practical eye-openers for many Claude AI tools and tricks. The session pushed me to purchase a Claude Pro subscription and use it for my personal products. I followed up by attending the Agent harness session. Looking forward to the Claude Data Architect session.
Hands-on experience was great. It was great learning about skills, tools, and sub agents.
Architecture-first AI development, building maintainable codebases, and the importance of clear requirements, thinking beyond rapid development.
Spec-driven development using Claude, LLM integration, Skills usage, and Hooks.
Very nice and interactive.
Here is what this day is worth, line by line.
No. You need to be comfortable writing code and working with APIs. Everything ML-specific, embeddings, retrieval, evaluation, is taught from first principles inside the workshop.
Open, swappable components throughout. You'll learn the pattern behind each pipeline, not a single vendor's SDK, so the architecture transfers to whatever your company already runs. Bring your laptop and a Claude subscription.
Architecture is a discipline that sharpens in rooms where engineers push back on each other's assumptions. The real value of this day is not the content alone; it's implementing a real AI stack with expert oversight in a space where every question gets a direct, technically honest answer. Online courses cannot replicate that.
One full day, 9:30 AM to 5:30 PM. No advance prep required beyond bringing a laptop and an active Claude subscription; every pipeline gets built live, during the session.
We teach the decision framework for all three approaches: prompting alone, retrieval-augmented generation, and fine-tuning, including when each one actually earns its cost. The system you build live is RAG-based, because that's the right fit for most enterprise data problems. You'll leave knowing exactly when to reach for fine-tuning instead.
Yes. A full refund is available up to 7 days before the workshop date. After that, your seat is non-refundable but transferable to a teammate.
Real implementation. Real enterprise scenarios. Real expert. 40 seats only.
Peninsular Research Operation
3rd Floor, 319, Babu Mudali St, Ellaiamman Colony, Teynampet, Chennai 600086 · 🚇 DMS Metro