Sign in Subscribe

From Zero to Production: A Project-Based Roadmap for AI Application Engineers

Last updated on 
This tutorial is a hands-on roadmap to grow from “I can build a quick demo” to “I can run, monitor, and ship AI products.”
It’s organized as a sequence of increasingly challenging projects. Each project has: Goal → Tech Stack → What to Build → Acceptance Criteria → Upgrade Trigger.
You can follow it end-to-end or cherry-pick stages that fit your current needs.

Who this is for

  • AI application engineers, full-stack devs, or data scientists who want production-grade LLM/RAG systems.
  • Teams adopting LangChain / LlamaIndex / LangGraph, vector databases, and self-hosted inference (vLLM/TGI).
  • Anyone who wants a portfolio path that proves real engineering depth.

Project Ladder (P0 → P8)

P0 — One-Night Win: Minimal RAG Prototype

Goal: Turn PDFs/Markdown into a question-answering demo.
Stack: LangChain or LlamaIndex, OpenAI/Claude (any LLM), FAISS/Chroma, Streamlit/Gradio.
Build:

  • Ingest docs → chunk → embed → vector store.
  • Simple chat UI; prompt template (system + few-shot).
    Acceptance:
  • A tiny gold QA set gets ≥60% retrieval hit rate (top-k).
  • No crashes; logs printed to console.
    Upgrade: You want persistence, APIs, and multi-user access.

P1 — Service-Ready RAG: API + Persistent Vector DB

Goal: Make the prototype callable as a service.
Stack: FastAPI, Docker, Postgres + PgVector, LangChain/LlamaIndex.
Build:

  • /ingest (incremental ingestion) and /ask (RAG) endpoints.
  • Docker Compose for API + Postgres.
  • API keys & simple rate limits.
    Acceptance:
  • Handles ~20 QPS; p95 latency < 2s (small contexts).
  • One-command startup via Docker.
    Upgrade: You need measurable quality and regression protection.

P2 — Quality Baseline: Evaluation & Regression

Goal: Make “good vs bad” measurable and repeatable.
Stack: Ragas or DeepEval, pytest, GitHub Actions.
Build:

  • Gold QA dataset; evaluation script reporting:
    • Answer Correctness, Context Precision/Recall, Hallucination Rate
  • CI runs eval on every PR; fails if below threshold.
  • Export HTML/Markdown reports.
    Acceptance:
  • Thresholds (e.g., Correctness ≥ 0.70; Context ≥ 0.70) enforced in CI.
  • Historical comparison available in artifacts.
    Upgrade: You need multi-step tools, state, and robust retries.

P3 — LangGraph Agents: Stateful, Multi-Tool, Recoverable

Goal: Turn “chat” into a deterministic state machine.
Stack: LangGraph (nodes/edges/checkpoints/retries), tool calling (search, SQL, code), memory compression.
Build:

  • Graph: Planner → Retriever → ToolCaller → Verifier → Reporter.
  • Retry & rollback branches; conversation memory via summarization.
  • Tracing on every node I/O for debuggability.
    Acceptance:
  • Complex tasks succeed ≥80% (e.g., “find 3 papers and compare them”).
  • You can replay and explain any failed run.
    Upgrade: You want lower latency, higher throughput, and cheaper inference.

P4 — High-Performance Inference: vLLM/TGI + Cost Routing

Goal: Cut cost, raise throughput, keep quality.
Stack: vLLM or TGI, LiteLLM/OpenRouter-style routing, continuous batching.
Build:

  • Self-host a base or instruction model (e.g., Llama/Mistral family).
  • A small router that chooses model by latency/price/quality.
  • A/B tests vs provider APIs; throughput benchmarks.
    Acceptance:
  • Throughput ↑ and cost/request ↓ with charts to prove it.
  • Seamless fallback to cloud if local capacity is hot.
    Upgrade: You need reliability, SLOs, and on-call-ready observability.

P5 — Observability & SLOs: Operate Like a Product

Goal: Debug in minutes, not days; protect user experience.
Stack: LangSmith or W&B Traces, Prometheus/Grafana, structured logs.
Build:

  • End-to-end traces: prompts, retrieved chunks, tool calls, LLM outputs.
  • Dashboards for QPS, p95/p99, error rate, retrieval hit rate, hallucination rate, cost per request.
  • SLOs (e.g., availability 99.5%, p95 < 2s, hallucination < 10%) + alerts.
    Acceptance:
  • Any incident is reproducible from traces.
  • Alerts fire on threshold breaches.
    Upgrade: You’re ready for team-wide pipelines, CI/CD, and data contracts.

P6 — Data & Pipeline Governance + CI/CD

Goal: Reliable data flows; safe, reversible deploys.
Stack: Airflow/Prefect, Great Expectations, GitHub Actions (CI/CD).
Build:

  • Full DAG: ingest → clean → chunk → embed → index (with incremental rebuild).
  • Data contracts: schema checks, null checks, distribution drift.
  • CI: unit + integration + eval → build image → blue/green deploy.
    Acceptance:
  • Failures rollback automatically; reruns are idempotent.
  • Data quality report is versioned and visible.
    Upgrade: Your org needs compliance and risk mitigation.

P7 — Security, Safety & Guardrails

Goal: Survive jailbreaks, injections, and data-leaks.
Stack: Guardrails/NeMo Guardrails, PII masking, RBAC, Vault/Doppler.
Build:

  • Prompt-injection & data-exfiltration test suites; automated red-team run.
  • Output validation (JSON Schema/regex/function checks) + refusals.
  • Role-based access to data domains; signed webhooks/allowlists.
    Acceptance:
  • ≥95% pass rate on adversarial suites; complete audit logs (who/when/what).
  • Secrets never live in code; all actions are traceable.
    Upgrade: Ship a real product that integrates with business systems.

P8 — Capstone: A Domain-Grade Assistant (Pick Your Theme)

Goal: Integrate everything into a cohesive product.
Examples:

  • Air-Cargo Knowledge Assistant: ingest standards (IATA MOP), papers, tables; hybrid retrieval; tools: SQL/Trino, pandas analysis, charting, bibliography.
  • Enterprise Knowledge & Workflow Copilot: contracts/policies, approvals (Jira/ServiceNow/email), strict guardrails.
  • Developer Doc Copilot: repo + API docs + issues/PRs; proposes fixes; opens PRs with approval gating.
    Must-haves:
  • LangGraph state machine (Plan → Execute → Verify → Report).
  • vLLM inference + routing; eval baseline + SLO + alerts.
  • CI/CD + data pipeline + audit & compliance.
    Deliverables:
  • Live demo, screenshots, eval dashboards, 3-minute product video, public repo.

Orchestration & Automation with n8n

Placement: n8n sits outside your core app as an automation layer (triggers, scheduling, notifications, approvals, external SaaS). Your core logic stays in your API/LangGraph.

Where to use it

  • P1: Webhook or cron to call /ingest; Slack notification on success/failure.
  • P2: Nightly eval → publish report → email/Slack.
  • P3: Start/finish/failed events → approvals, retries, incident tickets.
  • P4–P5: Read Prometheus/LangSmith; auto scale down/up or route to cheaper models; page on SLO breach.
  • P6: Watch storage changes → trigger Airflow DAG → rebuild index → deploy.
  • P7: Run jailbreak tests before release; block if fails; notify approvers.
  • P8: End-to-end “business event → agent workflow → approval → archive”.

Best practices

  • Idempotent APIs with X-Idempotency-Key.
  • n8n handles triggering, retries, approvals, alerts—not business rules.
  • Keep n8n private/VPN; HMAC-signed webhooks; secrets in n8n credentials.

A Reusable Repository Skeleton

repo/
├─ apps/
│  ├─ api/               # FastAPI (/ingest, /ask, /tools)
│  └─ ui/                # Streamlit/Next.js (optional)
├─ rag/
│  ├─ ingest/            # cleaning/chunking/embedding (Airflow/Prefect optional)
│  ├─ retrievers/        # BM25 / dense / hybrid / rerank
│  └─ prompts/           # system/retrieval/tool templates
├─ agents/
│  └─ graphs/            # LangGraph definitions & checkpoints
├─ eval/
│  ├─ datasets/          # gold QAs + adversarial suites
│  └─ run_eval.py        # Ragas/DeepEval entrypoint
├─ infra/
│  ├─ docker/            # Dockerfile, compose
│  ├─ k8s/               # (optional) Helm charts
│  └─ observability/     # Prometheus/Grafana/LangSmith configs
├─ tests/                # unit, integration, regression
├─ scripts/              # one-click tasks: ingest/eval/deploy
└─ README.md

Minimal .env

OPENAI_API_KEY=...
DB_URL=postgresql://user:pass@db:5432/rag
EMBED_MODEL=bge-small-en
VECTOR_DIM=384
INDEX_TYPE=pgvector

Unified Metrics (reuse from P2 onward)

  • Answer Correctness (semantic; LLM-as-judge/Ragas)
  • Context Precision / Recall
  • Hallucination Rate
  • Latency p95 / Cost per Request
  • Tool-Use Success
  • Task Success Rate (end-to-end)

Suggested Timeline (example)

  • Week 1: P0 → P1 (demo → API + PgVector)
  • Week 2: P2 (eval baseline + CI)
  • Weeks 3–4: P3 (LangGraph agent + retries + memory)
  • Week 5: P4 (vLLM/TGI + routing + benchmarks)
  • Week 6: P5 (traces, dashboards, SLOs)
  • Weeks 7–8: P6–P7 (pipelines, data contracts, guardrails)
  • Weeks 9–10: P8 Capstone (end-to-end product, video, write-up)

What to Publish in Your Portfolio

  • Per-project: README with architecture diagram, demo GIF, key metrics, “why we upgraded” notes.
  • Comparisons: chunking/embedding/model/routing A/B charts.
  • Cost & throughput: API vs self-hosted vLLM curves.
  • Security: jailbreak suite results, refusal patterns, audit samples.
  • Operations: LangGraph visualizations, LangSmith traces, Grafana panels.

Final Note

This roadmap is opinionated but battle-tested: start simple, measure, add state & tools when needed, then harden with observability, pipelines, and guardrails. Use n8n as the automation shell around your core—which stays in FastAPI + LangGraph + Vector DB + vLLM.