GenAI Engineering for Business Processes
Design, build, and ship LLM-powered workflows that make real teams faster — securely and reliably.
What you’ll learn
- Problem framing → ROI: map a business process, define success metrics (SLA, CSAT, cycle time), and pick the right LLM approach (prompting vs RAG vs fine-tuning vs tools/agents).
- LLM essentials for builders: tokens & context, embeddings, attention at a glance; structured output with JSON schemas/Pydantic, function calling, and tool use.
- Quality guardrails: grounding checks, self-verification, confidence scoring, human-in-the-loop
- Cost & latency control: model selection, token budgets, caching, streaming
- Operations: tracing & monitoring (prompts, versions, feedback), test pipelines, incident playbooks.
Hands-on application
- Project 1 — Support RAG bot: ingest a knowledge base, answer & cite, escalate on low confidence, and log every trace; Slack/Teams integration.
- Project 2 — Document automation: extract fields from invoices/contracts with structured output + validators; push to ERP/Sheets; handle exceptions with an approval step.
- Project 3 — Sales/ops co-pilot: summarize calls/emails, extract tasks, draft replies, and update CRM via tool calling; add a cost/latency dashboard.
Prerequisites
- Comfortable with Python, Git, REST APIs, and basic cloud/deployment.
- Familiarity with training simple neural nets helpful but not required.
Who it’s for
- Software engineers, data/ML engineers, and product/ops leads embedding LLMs into customer support, finance, HR, IT, and sales processes.
Formats
- Two-Day Intensive (12–14h): day 1 foundations + RAG; day 2 orchestration, guardrails, deployment.
- Corporate Workshop: customized to your stack (cloud, data stores, auth, CI/CD) and your target processes.