Rohit Bhardwaj

System Design AI Mastery: Architecting for Scale, Speed, Reliability - Full Day

Modern system design has entered a new era. It’s no longer enough to optimize for uptime and latency — today’s systems must also be AI-ready, token-efficient, trustworthy, and resilient. Whether building global-scale apps, powering recommendation engines, or integrating GenAI agents, architects need new skills and playbooks to design for scale, speed, and reliability.

This full-day workshop blends classic distributed systems knowledge with AI-native thinking. Through case studies, frameworks, and hands-on design sessions, you’ll learn to design systems that balance performance, cost, resilience, and truthfulness — and walk away with reusable templates you can apply to interviews and real-world architectures.

Target Audience

Enterprise & Cloud Architects → building large-scale, AI-ready systems.

Backend Engineers & Tech Leads → leveling up to system design mastery.

AI/ML & Data Engineers → extending beyond pipelines to full-stack AI systems.

FAANG & Big Tech Interview Candidates → preparing for system design interviews with an AI twist.

Engineering Managers & CTO-track Leaders → guiding teams through AI adoption.

Startup Founders & Builders → scaling AI products without burning money.

Learning Outcomes

By the end of the workshop, participants will be able to:

Apply a 7-step system design framework extended for AI workloads.

Design systems that scale for both requests and tokens.

Architect multi-provider failover and graceful degradation ladders.

Engineer RAG 2.0 pipelines with hybrid search, GraphRAG, and semantic caching.

Implement AI trust & security with guardrails, sandboxing, and red-teaming.

Build observability dashboards for hallucination %, drift, token costs.

Reimagine real-world platforms (Uber, Netflix, Twitter, Instagram) with AI integration.

Practice mock interviews & chaos drills to defend trade-offs under pressure.

Take home reusable templates (AI System Design Canvas, RAG Checklist, Chaos Runbook).

Gain the confidence to lead AI-era system design in interviews, enterprises, or startups.

Workshop Agenda (Full-Day, 8 Hours)
Session 1 – Foundations of Modern System Design (60 min)

The new era: Why classic design is no longer enough.

Architecture KPIs in the AI age: latency, tokens, hallucination %, cost.

Group activity: brainstorm new KPIs.

Session 2 – Frameworks & Mindset (75 min)

The 7-Step System Design Framework (AI-extended).

Scaling humans vs tokens.

Token capacity planning exercise.

Session 3 – Retrieval & Resilience (75 min)

RAG 2.0 patterns: chunking, hybrid retrieval, GraphRAG, semantic cache.

Multi-provider resilience + graceful degradation ladders.

Whiteboard lab: design a resilient RAG pipeline.

Session 4 – Security & Observability (60 min)

Threats: prompt injection, data exfiltration, abuse.

Guardrails, sandboxing, red-teaming.

Observability for LLMs: traces, cost dashboards, drift monitoring.

Activity: STRIDE threat-modeling for an LLM endpoint.

Session 5 – Real-World System Patterns (90 min)

Uber, Netflix, Instagram, Twitter, Search, Fraud detection, Chatbot.

AI-enhanced vs classic system designs.

Breakout lab: redesign a system with AI augmentation.

Session 6 – Interviews & Chaos Drills (75 min)

Mock interview challenges: travel assistant, vector store sharding.

Peer review of trade-offs, diagrams, storytelling.

Chaos drills: provider outage, token overruns, fallback runbooks.

Closing (15 min)

Recap: 3 secrets (Scaling tokens, RAG as index, Resilient degradation).

Templates & takeaways: AI System Design Canvas, RAG Checklist, Chaos Runbook.

Q&A + networking.

Takeaways for Participants

AI System Design Canvas (framework for interviews & real-world reviews).

RAG 2.0 Checklist (end-to-end retrieval playbook).

Chaos Runbook Template (resilience drill starter kit).

AI SLO Dashboard template for observability + FinOps.

Confidence to design and defend AI-ready architectures in both career and enterprise contexts.

AI Inference at Scale: Reliability, Observability, Cost & Sustainability

AI inference is no longer a simple model call—it is a multi-hop DAG of planners, retrievers, vector searches, large models, tools, and agent loops. With this complexity comes new failure modes: tail-latency blowups, silent retry storms, vector store cold partitions, GPU queue saturation, exponential cost curves, and unmeasured carbon impact.

In this talk, we unveil ROCS-Loop, a practical architecture designed to close the four critical loops of enterprise AI:
•Reliability (Predictable latency, controlled queues, resilient routing)
•Observability (Full DAG tracing, prompt spans, vector metrics, GPU queue depth)
•Cost-Awareness (Token budgets, model tiering, cost attribution, spot/preemptible strategies)
•Sustainability (SCI metrics, carbon-aware routing, efficient hardware, eliminating unnecessary work)

KEY TAKEAWAYS
•Understand the four forces behind AI outages (latency, visibility, cost, carbon).
•Learn the ROCS-Loop framework for enterprise-grade AI reliability.
•Apply 19 practical patterns to reduce P99, prevent retry storms, and control GPU spend.
•Gain a clear view of vector store + agent observability and GPU queue metrics.
•Learn how ROCS-Loop maps to GCP, Azure, Databricks, FinOps & SCI.
•Leave with a 30-day action plan to stabilize your AI workloads.

⸻

AGENDA
1.The Quiet Outage: Why AI inference fails
2.X-Ray of the inference pipeline (RAG, agents, vector, GPUs)
3.Introducing the ROCS-Loop framework
4.19 patterns for Reliability, Observability, FinOps & GreenOps
5.Cross-cloud mapping (GCP, Azure, Databricks)
6.Hands-on: Diagnose an outage with ROCS
7.Your 30-day ROCS stabilization plan
8.Closing: Becoming a ROCS AI Architect

Architecting Microservices for Agentic AI Integration

Autonomous LLM agents don’t just call APIs — they plan, retry, chain, and orchestrate across multiple services.
That fundamentally changes how we architect microservices, define boundaries, and operate distributed systems.
This session delivers a practical architecture playbook for Agentic AI integration — showing how to evolve from simple request/response designs to resilient, event-driven systems.
You’ll learn how to handle retry storms, contain failures with circuit breakers and bulkheads, implement sagas and outbox patterns for correctness, and version APIs safely for long-lived agents.
You’ll leave with reference patterns, guardrails, and operational KPIs to integrate agents confidently—without breaking production systems.

Problems Solved

• Microservices collapse under agent retries or fan-out behavior
• Lack of event logs or compensations breaks agent re-planning
• Failures cascade due to missing bulkheads or circuit breakers
• Non-deterministic APIs cause unpredictable agent actions
• Ops teams can’t separate or monitor agent vs human traffic

Why Now

• Agentic frameworks (Agentforce, LangGraph, CrewAI) are entering production.
• Traditional microservices assume human or synchronous clients — not autonomous retriers.
• Reliability, determinism, and observability must now be built into API contracts.
• Agent traffic adds new stress patterns and compliance visibility requirements.

What Is Agentic AI in Microservices

• Agents plan, retry, and chain service calls — requiring deterministic, idempotent APIs.
• Services must be tool-callable (stable operationId, strict input/output schemas).
• Systems must survive retry storms, fan-out, and long-lived sessions.

Agenda
Opening: The Shift to Agent-Driven Systems
How autonomous agents change microservice assumptions.
Why request/response architectures fail when faced with planning, chaining, and self-healing agents.

Pattern 1: Event-Driven Flows
Use events, queues, and replay-safe designs to decouple agents from synchronous APIs.
Patterns: pub/sub, event sourcing, and replay-idempotency.

Pattern 2: Saga and Outbox Patterns
Manage long workflows with compensations.
Ensure atomicity and reliability between DB and event bus.
Outbox → reliable publish; Saga → rollback on failure.

Pattern 3: Circuit Breakers and Bulkheads
Contain agent-triggered failure storms.
Apply timeout, retry, and fallback policies per domain.
Prevent blast-radius amplification across services.

Pattern 4: Service Boundary Design
Shape services around tasks and domains — not low-level entities.
Example: ReserveInventory, ScheduleAppointment, SubmitClaim.
Responses must return reason codes + next actions for agent clarity.
Avoid polymorphic or shape-shifting payloads.

Pattern 5: Integrating Agent Frameworks
Connect LLM frameworks (Agentforce, LangGraph) safely to services.
Use operationId as the agent tool name; enforce strict schemas.
Supervisor/planner checks between steps.
Asynchronous jobs: job IDs, progress endpoints, webhooks.

Pattern 6: Infrastructure and Operations

• Observability: Tag agent runs (x-agent-run-id), trace retries, success/failure.
• Versioning: Use SemVer, deprecation headers, and multi-version gateways.
• Resilience: Autoscale on retry rate, degrade gracefully, and run failover drills.

Wrap-Up: KPIs and Guardrails for Production
Key metrics: retry rate, success ratio, agent throughput, event replay lag.
Lifecycle governance: monitoring, versioning, deprecation, and sunset plans.

Key Framework References

• Salesforce Agentforce – agentic orchestration and guardrail templates
• LangGraph / CrewAI – multi-agent planning and coordination patterns
• Cloud Native Patterns: Saga, Outbox, Circuit Breaker, Bulkhead, Event-Driven Architecture
• OpenTelemetry + Prometheus: Observability for agent vs human traffic
• OWASP LLM Top-10: Guardrails for safe function calling and data handling

Takeaways

• Blueprint for agent-friendly microservices architecture
• Patterns for event-driven, saga, and outbox consistency
• Guardrails: circuit breakers, bulkheads, least privilege APIs
• Framework integration checklist (Agentforce, LangGraph, etc.)
• Ops playbook for observability, versioning, and resilience
• KPIs to measure readiness: retry rate, grounding accuracy, and agent success ratio

Beyond APIs: Orchestration Patterns & MCP for Multi-Agent Systems

Enterprises are moving from single AI agents to networks of agents that trigger thousands of API calls, retries, and tool-chains per prompt. Without orchestration discipline and APIs built for AI-scale, systems buckle under bursty load, retry storms, cache-miss spikes, inconsistent decisions, and runaway costs.

This talk shows how to combine MCP (Model Context Protocol) with proven inter-agent orchestration patterns — Supervisor, Pub/Sub, Blackboard, Capability Router — and how to harden APIs for autonomous traffic using rate limits, dedupe, backpressure, async workflows, resilient caching, and autoscaling without bill shock.

You’ll also learn the AIRLOCK Framework for governing multi-agent behavior with access boundaries, identity checks, rate controls, least-privilege routing, observability, compliance filters, and kill-switches.

You will walk away with a practical blueprint for building multi-agent systems that are fast, safe, reliable, and cost-predictable.

KEY TAKEAWAYS
Pattern Literacy: When to use Orchestrator, Pub/Sub, Blackboard, Router

MCP Fluency: Standardize agent↔tool integration

API Scaling: Rate limits, dedupe, backpressure, async, caching

Resilience: Bulkheads, jitter, circuit breakers, autoscaling guardrails

Observability: Trace chain-ID/tool-ID across agents & tools

AIRLOCK Governance: Access boundaries, identity, rate controls, least-privilege routing, compliance, kill-switches

AGENDA

• Why AI Changes Load Patterns
  Bursty workloads · fan-out · retry amplification · cost spikes

• MCP 101
  Standardized agent→tool access · hot-swappable tools

• Orchestration Patterns
  Supervisor · Pub/Sub · Blackboard · Capability Router

• Architecting APIs for AI Traffic
  Multi-dimensional rate limits · dedupe · backpressure · SWR caching · async

• Resilience & Autoscaling
  Circuit breakers · bulkheads · kill-switches · budget caps

• Observability & Governance
  Chain-ID tracing · anomaly detection · AIRLOCK boundaries

Run an AWS Generative-AI Well-Architected Review

A live, end-to-end walkthrough of an AWS Well-Architected Review for a GenAI app. You’ll learn how to apply the AWS Generative AI Lens across the six pillars, then add Bedrock Guardrails and Knowledge Bases (RAG) to raise reliability, safety, and accuracy. You’ll leave with a reusable checklist and a prioritized remediation plan.

Who it’s for & why

• Cloud architects, AI/ML engineers, SRE/Platform teams, and security leads
• Moving from PoCs → production with need for a repeatable WA review process

What you’ll learn

• How to apply Generative AI Lens questions per pillar
• Spot hot spots: data flow, prompts, vector DBs, GPU scaling
• Map risks → fixes with Guardrails + RAG patterns

What you’ll take away

• WA Review scorecard & template
• Pillar-by-pillar remediation backlog
• Sample Guardrail policies (safety, PII, toxic output)
• RAG/Knowledge Base reference architecture

Graph Thinking with AI: Algorithms that Power Real Systems

Graphs aren’t just academic—they power the backbone of real systems: workflows (Airflow DAGs), build pipelines (Bazel), data processing (Spark DAGs), and microservice dependencies (Jaeger).
This session demystifies classic graph algorithms—BFS, DFS, topological sort, shortest paths, and cycle detection—and shows how to connect them to real-world systems.
You’ll also see how AI tools like ChatGPT and graph libraries (Graphviz, NetworkX, D3) can accelerate your workflow: generating adjacency lists, visualizing dependencies, and producing test cases in seconds.
You’ll leave with reusable patterns for interviews, architecture reviews, and production systems.

You’ll leave with reusable patterns for interviews, architecture reviews, and production systems.

Why Now

• Graphs underpin the modern stack: Airflow DAGs, Spark, Kubernetes, Jaeger, and CI/CD pipelines.
• AI tools can rapidly generate, visualize, and validate graph structures—bridging algorithmic theory and practical engineering.
• Graph literacy now distinguishes great developers from average system designers.

Problems Solved

• Translating algorithmic concepts into production patterns
• Lack of intuition about DAGs, dependency graphs, or routing systems
• Difficulty visualizing large graph structures quickly
• Limited practice applying BFS/DFS/topo sort outside interview prep

Learning Outcomes

• Apply BFS/DFS, topological sort, and shortest path algorithms in production systems
• Translate graph theory into schedulers, dependency analyzers, and routing services
• Use AI (ChatGPT/Copilot) for quick generation of adjacency lists, test data, and graph visualization
• Map graphs to system design conversations: latency, scaling, dependencies
• Build your own reusable Graph Thinking toolkit for architecture and interviews

Agenda
Opening: From Whiteboard to Production
Why every large-scale system is a graph in disguise.
How workflows, microservices, and dependency managers rely on graph structures.
Pattern 1: Graphs in the Real World
Examples:

• Workflows: Airflow, Dagster
• Builds: Bazel
• Data pipelines: Spark
• Services: Jaeger tracing DAGs
Show how each maps to graph nodes, edges, and cycles.

Pattern 2: Core Algorithms Refresher

• BFS/DFS: Reachability, search, and crawl use cases
• Dijkstra / A*: Routing, latency, and cost optimization
• Topological Sort: Scheduling builds, DAG execution order
• Cycle Detection: Fail-fast prevention in workflows and dependency graphs

Pattern 3: AI-Assisted Graph Engineering
How to use AI tools to accelerate graph work:

• Generate adjacency lists from plain-text prompts
• Auto-create test cases for reachability and cycle detection
• Use Graphviz / NetworkX / D3 to visualize graphs instantly
• Validate algorithm correctness interactively

Pattern 4: Graph Patterns in Architecture
Mapping algorithms to system design:

• BFS → discovery & dependency mapping
• DFS → deep audits & lineage analysis
• Dijkstra → route optimization & latency modeling
• Topo sort → job orchestration & CI/CD scheduling
How architects can embed graph thinking into design reviews.

Pattern 5: AI Demo
Prompt → adjacency list → Graphviz/NetworkX render → algorithmic validation.
Demonstrate quick prototyping workflow with AI assistance.

Wrap-Up: From Algorithms to Architectural Intuition
How graph literacy improves system reliability and scalability.
Checklist and reusable templates for ongoing graph-based reasoning.

Key Framework References

• NetworkX / Graphviz / D3.js: Visualization and validation libraries
• Apache Airflow / Spark / Bazel / Jaeger: Real-world DAG examples
• AI Tools (ChatGPT, Copilot): Adjacency generation, testing, and explanation
• Big-O Foundations: BFS/DFS/Dijkstra complexity reminders for performance analysis

Takeaways

• Graph Thinking Checklist: Nodes, edges, cycles, and DAG validation
• AI Prompt Pack: Templates for adjacency generation, test creation, and visualization
• Algorithm Snippet Starter Kit: BFS, DFS, Dijkstra, Topo Sort in Python/JS
• Architecture Mapping Guide: Graph patterns → system use cases
• Mindset: Move from memorizing algorithms → to engineering with them

GraphRAG & Explainable AI: Building Trustworthy LLM Outputs

Most enterprise LLM failures aren’t technical — they’re trust failures. Models hallucinate, drift from source truth, or produce outputs with no provenance. For regulated industries, that’s unacceptable.
This session introduces GraphRAG — a breakthrough approach combining knowledge graphs (Neo4j) with retrieval-augmented generation to deliver traceable, explainable, and auditable AI outputs.
You’ll learn how to design, evaluate, and deploy GraphRAG architectures aligned with the EU AI Act, NIST AI Risk Management Framework, and enterprise AI governance standards.

Problems Solved

• LLM answers without evidence or traceability
• Stale or inconsistent retrieval data
• Non-compliance with transparency and provenance regulations
• Lack of explainability for model outputs
• Low confidence from regulators, auditors, and executives

Why Now

• Enterprise AI adoption slowed by lack of trust and explainability
• Regulations (EU AI Act, NIST AI RMF) now require provenance and model transparency
• Executives demand evidence-based reasoning, not black-box answers

What GraphRAG Is

• Combines knowledge graphs (Neo4j) with retrieval-augmented generation
• Returns answers with structured evidence paths — connecting entities → relationships → source documents → LLM response
• Goes beyond flat vector search to capture contextual meaning, hierarchy, and causality

Where It Applies

• Insurance: Claims approvals and denials with transparent justification
• Healthcare: Patient summaries with provenance and compliance
• Finance: Audit trails, credit-risk reasoning, regulatory reporting
• Policy & Legal: Regulatory interpretation and case law summaries

Why It’s Valuable

• Establishes trust with executives, auditors, and regulators
• Improves faithfulness, groundedness, and transparency of model outputs
• Reduces disputes, compliance risks, and hallucination-related rework
• Creates structured AI reasoning pipelines aligned with governance frameworks

Agenda
Opening & Problem Context
Why trust is the bottleneck for enterprise AI.
Examples of LLMs failing in regulated use cases — what breaks when outputs lack provenance.
Pattern 1: Anatomy of GraphRAG
Understanding how GraphRAG extends RAG with Neo4j graphs.
Schema design for entities, relationships, and evidence paths.
Structured retrieval from graph → vector → generator.

Pattern 2: Architecture & Data Flow
End-to-end GraphRAG blueprint:
Ingestion → Entity extraction → Graph population → Retrieval orchestration → Response grounding.
Contrast with plain RAG and vector-only approaches.

Pattern 3: Explainability & Evaluation
Metrics for evaluating explainability:
Faithfulness, groundedness, and coverage.
How to trace model answers back to graph nodes and documents.
Integration with AI observability platforms (PromptLayer, Arize, etc.).

Pattern 4: Compliance & Governance Alignment
Connecting GraphRAG design to regulatory frameworks:

• EU AI Act: Transparency, traceability, human oversight
• NIST AI RMF: Trustworthiness and accountability
• ISO 42001: AI Management Systems
Implementing provenance tags and explainability layers as compliance enablers.

Pattern 5: Real-World Scenarios
Industry case patterns:

• “Why was this insurance claim denied?”
• “Which regulation does this contract violate?”
• “Which patient data contributed to this summary?”
Each example maps relationships, evidence, and trace paths through Neo4j.

Wrap-Up & Discussion
Recap of GraphRAG architecture and design patterns.
Checklist for adoption: schema templates, metrics, and governance integration.
Q/A and enterprise discussion on explainable AI roadmaps.

Key Framework References

• Microsoft GraphRAG: Open-source structured hierarchical retrieval pattern
• Neo4j Graph Data Science & LLM Integration Guide
• EU AI Act & NIST AI RMF: Provenance, explainability, and risk transparency
• ISO/IEC 42001: AI governance and management principles
• Gartner & Forrester: Trust and transparency as core adoption barriers

Takeaways

• GraphRAG design blueprint (schema + ingestion + retriever)
• Evaluation metrics: faithfulness, groundedness, coverage
• Reference architecture diagrams for Neo4j + RAG + LLM stack
• Playbook for integrating explainability with compliance frameworks

From Brute Force to Brilliance: Algorithmic Thinking in the Age of AI

Coding interviews and production systems share the same challenge: transforming vague problems into correct, efficient, and explainable solutions.
This talk introduces a 7-step algorithmic thinking framework that begins with a brute-force baseline and evolves toward an optimized, production-grade solution—using AI assistants like ChatGPT and GitHub Copilot to accelerate ideation, edge-case discovery, and documentation, without sacrificing rigor.
Whether you’re solving array or graph problems, optimizing data pipelines, or refactoring legacy logic, this framework builds the discipline of clarity before optimization—and shows how to use AI responsibly as a thinking partner, not a shortcut.

Why This Talk Now (in the AI Era)

• AI is already in your workflow: 51% of professional developers use AI tools daily; 84% plan to adopt. (Stack Overflow Developer Survey)
• AI boosts productivity, but needs structure: Controlled studies show developers complete tasks ~56% faster with GitHub Copilot—but correctness still requires disciplined reasoning. (arXiv)
• Engineering leaders demand ROI + rigor: 71% of organizations report regular GenAI use, but need trustworthy frameworks to reduce “hallucination debt.” (McKinsey)
• Interviews still test DS&A: Problem-solving frameworks outperform memorization. (Google Tech Dev Guide)

Problems Solved

• Unclear or incomplete problem statements
• Over-reliance on AI code suggestions without validation
• Jumping to optimization before correctness
• Failing to reason about time/space complexity
• Difficulty communicating trade-offs in reviews or interviews

The 7-Step Algorithmic Thinking Playbook

1. Clarify – Define inputs, outputs, and constraints precisely.

2. Baseline – Write the simplest brute-force solution for correctness.

3. Measure – Analyze time and space complexity; identify bottlenecks.

4. Map Patterns – Recognize the family (array, tree, graph, DP, greedy).

5. Refactor – Apply the optimal pattern or data structure.

6. Validate – Test edge cases and boundary conditions automatically.

7. Explain – Communicate trade-offs, scalability, and readability.

Learning Outcomes

• Apply a repeatable, 7-step problem-solving framework for any coding challenge.
• Know when brute force is acceptable—and when optimization matters.
• Confidently compare greedy vs. DP or iterative vs. recursive strategies.
• Use AI tools responsibly for ideation, validation, and refactoring.
• Communicate algorithmic reasoning clearly in code reviews and interviews.

Agenda
Opening: The AI-Accelerated Engineer
How AI is reshaping developer workflows—and why algorithmic clarity matters more than ever.
Examples of AI code that’s correct syntactically but wrong logically.

Pattern 1: Clarify and Baseline
Turning vague questions into crisp specifications.
Why starting with brute force improves correctness and confidence.

Pattern 2: Measure and Map Patterns
How to quickly estimate complexity and identify known solution families.
Mapping problems to arrays, graphs, or DP templates.

Pattern 3: Refactor with AI as a Partner
Using Copilot or ChatGPT to suggest refactors, not replace reasoning.
Prompt patterns for safe collaboration (“generate + verify + explain”).
Spotting hallucinated optimizations.

Pattern 4: Validate and Explain
Building automated test scaffolds and benchmark harnesses.
AI-assisted edge-case discovery.
How to articulate trade-offs in interviews or design docs.

Pattern 5: Framework in Action
Live problem walkthrough:
From brute-force substring search → optimized sliding window solution → complexity and trade-off explanation.
Demonstrate where AI adds value and where human logic rules.

Pattern 6: Guardrails for AI-Assisted Coding
Version control hygiene, reproducibility, test coverage.
Ensuring deterministic, reviewable AI suggestions.
Avoiding “hallucination debt” in production codebases.

Wrap-Up: From Algorithms to Systems Thinking
How this framework extends from whiteboard problems to microservices, pipelines, and data workflows.
Checklist for using AI as a disciplined amplifier of human reasoning.

Key Framework References

• Stack Overflow Developer Survey (2024) – AI adoption statistics
• GitHub Copilot Research – Productivity vs correctness studies
• McKinsey State of AI Report – ROI benchmarks in engineering teams
• Google Tech Dev Guide – Problem-solving and DS&A frameworks
• IEEE/ACM Ethical AI Practices – Human-in-the-loop coding

Takeaways

• 7-Step Algorithmic Thinking Framework — printable reference card
• AI Guardrails Checklist for safe Copilot/ChatGPT use in code and reviews
• Prompt Templates for structured ideation, verification, and documentation
• Live Case Study Walkthrough for clarity, optimization, and explanation
• A mindset shift: from memorizing algorithms → to designing reasoning systems

Dynamic Programming Demystified: How AI Helps You See the Pattern

Dynamic Programming (DP) intimidates even seasoned engineers. With the right lens, it’s just optimal substructure + overlapping subproblems turned into code. In this talk, we start from a brute-force recursive baseline, surface the recurrence, convert it to memoization and tabulation, and connect it to real systems (resource allocation, routing, caching). Along the way you’ll see how to use AI tools (ChatGPT, Copilot) to propose recurrences, generate edge cases, and draft tests—while you retain ownership of correctness and complexity. Expect pragmatic patterns you can reuse in interviews and production.

Why Now

• DP = #1 fear topic in interviews.
• Used in systems: caching, routing, scheduling.
• 55% faster with Copilot, but needs guardrails.
• AI adoption is surging — structure required.

Key Framework

• Find optimal substructure.
• Spot overlapping subproblems.
• Start brute force → derive recurrence.
• Memoization → tabulation.
• Compare vs. greedy & divide-and-conquer.
• Use AI for tests & recurrences, not correctness.

Core Content

• Coin Change: brute force → DP; greedy fails in non-canonical coins.
• 0/1 Knapsack: DP works, greedy fails; fractional knapsack = greedy.
• LIS: O(n²) DP vs. O(n log n) patience method.
• Graphs: shortest path as DP on DAGs.
• AI Demos: recurrence suggestion, edge-case generation.

Learning Outcomes

• Know when a problem is DP-worthy.
• Build recurrence → memoization → tabulation.
• Decide Greedy vs DP confidently.
• Apply AI prompts safely (tests, refactors).
• Map DP to real-world systems.