AI TRAINING
OpenAI API & Platform Essentials for Engineers
Engineers leave able to build, secure, and cost-optimise production integrations on the OpenAI platform.
What it covers
A hands-on technical programme covering the full OpenAI platform stack: REST API fundamentals, Chat Completions, the Assistants API, function calling, Realtime API, and file/vector store management. Participants learn to configure enterprise accounts, enforce data-handling policies, implement rate-limit and cost controls, and deploy reliable AI features in production environments. The course combines live coding sessions with structured exercises so engineers can apply concepts immediately. By the end, teams have working prototypes and repeatable patterns for OpenAI-powered features.
What you'll be able to do
- Authenticate and call OpenAI Chat Completions and Assistants APIs from production code with proper error handling and retries
- Design and implement function-calling flows that integrate external tools and data sources into an LLM pipeline
- Configure OpenAI enterprise account settings to enforce zero data retention and meet organisational data-handling requirements
- Build a token-budget strategy and instrumentation layer to keep monthly API spend within defined thresholds
- Evaluate model options (GPT-4o, GPT-4o-mini, o-series) on latency, cost, and quality to select the right fit for a given use case
Topics covered
- OpenAI REST API authentication, versioning, and SDKs (Python & Node.js)
- Chat Completions: system prompts, message history, streaming, and structured outputs
- Assistants API: threads, runs, tool use, and file search
- Function calling and tool orchestration patterns
- Realtime API: streaming audio and low-latency response design
- Token budgeting, model selection trade-offs, and cost monitoring
- Enterprise vs standard account: data retention policies, zero data retention, and privacy settings
- Rate limits, error handling, retries, and production resilience
Delivery
Delivered as a 2–3 day in-person or virtual bootcamp with a 70/30 hands-on-to-instruction ratio. Each module ends with a coding exercise in a shared environment (Jupyter or VS Code Live Share). Participants receive a starter repo, API credential sandbox, and a cost-monitoring dashboard template. Remote delivery uses breakout rooms for pair programming. Materials, recorded walkthroughs, and a Slack/Teams support channel are provided for 30 days post-training.
What makes it work
- Establish a shared API key management and secrets rotation policy before the first production deployment
- Instrument every API call with token count logging from day one to enable ongoing cost governance
- Run a design review checklist (model choice, context size, fallback behaviour) before merging any new AI feature
- Keep a dedicated sandbox project for experimentation so production quotas and data policies are never compromised
Common mistakes
- Ignoring token limits and context-window management until production latency and costs spiral
- Using personal or developer accounts in production, bypassing enterprise data-handling and privacy controls
- Hard-coding model names without a versioning strategy, leading to breaking changes when models are deprecated
- Treating the Assistants API as a drop-in replacement for Chat Completions without understanding threading and state-persistence costs
When NOT to take this
Teams that have not yet identified a concrete product use case for AI — they will accumulate API knowledge without a real problem to anchor it to, and adoption stalls within weeks of training ending.
Providers to consider
Sources
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.