A comprehensive, hands-on, 5-day training program for building, fine-tuning, and deploying production-ready applications on the OpenAI Platform. This Training takes participants from API fundamentals through advanced techniques — fine-tuning, retrieval-augmented generation, agentic tool use, and enterprise-grade deployment, observability, and cost/security governance.
- Overview
- Learning Objectives
- Audience
- Prerequisites
- Lab Environment
- Training Schedule
- Repository Structure
- Getting Started
- Lab Exercises
- Assessment & Certification
- Reference Architecture
- Troubleshooting Guide
- Additional Resources
- Contributing
- License
This training module equips engineering teams with the practical skills required to design, build, and operate applications on the OpenAI Platform in a real-world, production-grade environment. The course combines conceptual instruction with extensive hands-on labs, culminating in participants deploying a fully functional, observable, and secured AI application backed by a fine-tuned model and a retrieval pipeline.
By the end of this training, participants will be able to:
- Navigate the OpenAI Platform: API keys, projects, organizations, usage limits, and the model catalog
- Make robust API calls using the Responses/Chat Completions APIs, including streaming responses
- Design effective prompts and enforce reliable structured outputs (JSON mode / schema-constrained generation)
- Build Retrieval-Augmented Generation (RAG) pipelines using embeddings and a vector store
- Prepare datasets and run supervised fine-tuning jobs; evaluate fine-tuned models against baselines
- Implement function calling and tool use to build agentic workflows
- Orchestrate multi-step agents with guardrails, memory, and error handling
- Apply production best practices: rate limiting, retries/backoff, caching, and cost optimization
- Secure API usage: key management, data privacy controls, and abuse/content moderation
- Deploy an OpenAI-powered service behind a scalable, observable production architecture
- Monitor usage, latency, and cost; implement logging, tracing, and evaluation pipelines (evals)
- Backend/software engineers building AI-powered features or products
- ML/AI engineers moving from experimentation to production LLM systems
- DevOps/Platform engineers responsible for deploying and operating AI services
- Technical leads evaluating OpenAI Platform adoption for their organization
Required:
- Working proficiency in at least one programming language (Python or Node.js/TypeScript)
- Familiarity with REST APIs and JSON
- Basic command-line comfort (Git, shell)
Recommended:
- Prior exposure to containers (Docker) and basic cloud concepts
- Familiarity with async/concurrent programming patterns
- Basic understanding of databases (SQL and/or vector stores)
Not required but helpful: Prior experience with any LLM provider's API, basic ML/NLP concepts
Each participant is provisioned with an isolated lab environment consisting of:
| Component | Specification |
|---|---|
| Dev Workstation | Cloud IDE (VS Code Server) with Python 3.11+ and Node.js 20+ |
| OpenAI Access | Sandboxed API key with a capped usage budget per participant |
| Vector Store | Managed Postgres + pgvector (or hosted vector DB) instance |
| Deployment Target | Containerized environment (Docker) + Kubernetes namespace for Day 5 |
| Supporting Services | Git server, Redis (caching/rate limiting), observability stack (Prometheus/Grafana + tracing) |
| Access | Browser-based terminal/IDE, SSH keys for deployment targets |
💡 A cloud-based lab (AWS/Azure/GCP) or fully local (Docker Compose) option is provided — see
/labs/environment-setup.
Morning — Concepts
- OpenAI Platform overview: organizations, projects, API keys, and billing/usage dashboards
- Model catalog: reasoning vs. general-purpose models, context windows, modality support, and choosing the right model for a task
- The Responses API vs. Chat Completions API: request/response lifecycle
- Rate limits, tokens, and pricing fundamentals
- SDKs: Python and Node.js client libraries
Afternoon — Hands-on Labs
- Lab 1.1: Set up your API key, environment, and first authenticated request
- Lab 1.2: Build a simple chat application with streaming responses
- Lab 1.3: Explore token counting, context window management, and cost estimation
- Lab 1.4: Compare outputs and latency across different models for the same task
Deliverable: A working CLI or minimal web chat client calling the OpenAI API with streaming output.
Morning — Concepts
- Prompt engineering fundamentals: system/developer/user roles, few-shot examples, and prompt templates
- Structured Outputs: enforcing JSON Schema-conformant responses
- Embeddings: how they work and when to use them
- Retrieval-Augmented Generation (RAG) architecture: chunking, indexing, retrieval, and re-ranking
- Vector stores and similarity search strategies
Afternoon — Hands-on Labs
- Lab 2.1: Design and iterate on prompts for a support-ticket classification task
- Lab 2.2: Enforce Structured Outputs against a defined JSON Schema
- Lab 2.3: Generate embeddings for a document corpus and build a vector index
- Lab 2.4: Build a complete RAG pipeline: ingest → chunk → embed → retrieve → generate
- Lab 2.5: Evaluate retrieval quality and tune chunking/retrieval parameters
Deliverable: A RAG-powered Q&A service answering questions over a sample document set with cited sources.
Morning — Concepts
- When to fine-tune vs. prompt engineer vs. use RAG (decision framework)
- Preparing and validating fine-tuning datasets (JSONL format, quality guidelines)
- Supervised fine-tuning workflow: upload, train, monitor job status
- Hyperparameters: epochs, learning rate multiplier, batch size
- Evaluating fine-tuned models: comparing against the base model with a held-out test set
- Model versioning and rollback strategy
Afternoon — Hands-on Labs
- Lab 3.1: Prepare and validate a fine-tuning dataset from raw sample data
- Lab 3.2: Launch a fine-tuning job and monitor training/validation loss
- Lab 3.3: Evaluate the fine-tuned model against the base model using a scoring rubric
- Lab 3.4: Integrate the fine-tuned model into the Day 2 application and A/B compare results
- Lab 3.5: Build an automated eval harness to regression-test model behavior
Deliverable: A fine-tuned model with a documented evaluation report comparing it to baseline, plus an automated eval suite.
Morning — Concepts
- Function calling / tool use fundamentals: defining tool schemas, handling tool-call responses
- Building multi-step agentic workflows: planning, execution, reflection loops
- Managing conversation state and memory across turns
- Guardrails: input/output validation, content moderation, and handling hallucinated tool calls
- Parallel vs. sequential tool execution; error handling and retries
Afternoon — Hands-on Labs
- Lab 4.1: Define and register custom tools (e.g., a weather lookup, a database query function)
- Lab 4.2: Build a multi-turn agent that plans and executes a multi-step task using tools
- Lab 4.3: Add the Moderation API and input/output guardrails to the agent
- Lab 4.4: Implement conversation memory and session management
- Lab 4.5 (Mini-Capstone): Build an end-to-end agent that combines RAG (Day 2), a fine-tuned classifier (Day 3), and tool use to complete a realistic business workflow
Deliverable: A functioning agentic application combining retrieval, custom tools, and guardrails.
Morning — Concepts
- Production architecture patterns: API gateway, request queuing, caching layers
- Reliability: retries with exponential backoff, timeouts, circuit breakers, handling rate-limit errors
- Cost optimization: prompt caching, model selection strategy, batching requests
- Security: API key management/rotation, secrets storage, data privacy and retention controls, PII handling
- Observability: structured logging, distributed tracing, latency/cost dashboards
- Building continuous evaluation pipelines for production monitoring (regression detection, drift)
Afternoon — Hands-on Labs
- Lab 5.1: Containerize the application and deploy it to the Kubernetes lab namespace
- Lab 5.2: Implement rate-limit handling, retries/backoff, and a Redis-backed response cache
- Lab 5.3: Configure centralized secrets management for API keys and rotate credentials
- Lab 5.4: Set up observability — structured logs, request tracing, and Grafana dashboards for latency/cost/error rate
- Lab 5.5 (Capstone): Deploy the full application (RAG + fine-tuned model + agent + guardrails) to production configuration with monitoring, alerting, and a documented runbook
Deliverable (Capstone Project): A fully deployed, observable, cost-monitored, production-ready OpenAI-powered application with a complete operational runbook.
openai-platform-training/
├── README.md
├── slides/ # Day-by-day presentation decks
│ ├── day1-platform-fundamentals.pdf
│ ├── day2-prompting-and-rag.pdf
│ ├── day3-fine-tuning.pdf
│ ├── day4-agents-and-tools.pdf
│ └── day5-production-deployment.pdf
├── labs/
│ ├── environment-setup/ # Docker Compose / Terraform lab bootstrap
│ ├── day1/
│ ├── day2/
│ ├── day3/
│ ├── day4/
│ └── day5/
├── datasets/
│ ├── fine-tuning/ # Sample JSONL training/validation sets
│ └── rag-corpus/ # Sample document corpus for retrieval labs
├── app/
│ ├── api-client/ # Reference API client wrappers (Python/Node)
│ ├── rag-pipeline/
│ ├── agent/
│ └── eval-harness/
├── deployment/
│ ├── docker/
│ ├── kubernetes/
│ └── observability/ # Prometheus/Grafana configs, tracing setup
├── solutions/ # Reference solutions for each lab
└── docs/
├── troubleshooting.md
├── architecture-diagrams/
└── glossary.md
-
Clone this repository:
git clone https://github.com/<your-org>/openai-platform-training.git cd openai-platform-training
-
Provision your lab environment:
cd labs/environment-setup docker compose up -d -
Configure your API key:
cp .env.example .env # Add your sandboxed OPENAI_API_KEY to .env -
Verify prerequisites:
./labs/environment-setup/scripts/check-prereqs.sh
-
Start with Day 1:
cd labs/day1 cat README.md
Each day's lab folder (labs/dayN/) contains:
README.md— step-by-step instructions and success criteriastarter/— starting-point code and configshints.md— progressive hints for anyone who gets stuck- Corresponding reference solution in
solutions/dayN/
Labs are designed to be completed sequentially, with each day's application building on the previous day's work.
- Daily checkpoints: short knowledge checks at the end of each day
- Capstone project (Day 5): graded on functionality, reliability, security posture, and observability
- Certificate of completion issued to participants who complete all daily labs and the capstone project
The course builds toward the following production reference architecture:
┌─────────────────────┐
│ API Gateway / │
│ Load Balancer │
└──────────┬───────────┘
│
┌───────────▼────────────┐
│ Application Service │
│ (retries, backoff, │
│ rate-limit handling) │
└───┬──────────────┬──────┘
│ │
┌──────────▼───┐ ┌──────▼─────────┐
│ Redis Cache / │ │ Vector Store │
│ Rate Limiter │ │ (RAG corpus) │
└────────────────┘ └─────────────────┘
│
┌───▼─────────────────┐
│ OpenAI Platform │
│ (Fine-tuned model + │
│ base models) │
└───────────────────────┘
Sidecars: Secrets Manager, Prometheus/Grafana, Tracing, Eval Pipeline
See docs/architecture-diagrams/ for detailed diagrams per day.
Common issues and resolutions are documented in docs/troubleshooting.md, covering:
- Rate limit (429) errors and backoff strategies
- Fine-tuning job failures and dataset validation errors
- Structured Output schema validation failures
- Vector store retrieval returning irrelevant results
- Function-calling loops and malformed tool-call arguments
- Latency and cost spikes in production
- OpenAI Platform Documentation
- OpenAI API Reference
- Fine-tuning Guide
- Structured Outputs Guide
- Function Calling Guide
- OpenAI Cookbook (GitHub)
Contributions to improve labs, fix errata, or add advanced modules are welcome. Please open an issue or submit a pull request following the guidelines in CONTRIBUTING.md.
This training material is released under the MIT License.
Questions or feedback? Open an issue in this repository or contact the training team.