New in 2026 - Applied AI Engineering for the Enterprise

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Applied AI Engineering For the Enterprise is a hands-on, 5-day training program from Tech Skills Transformations designed to help teams move beyond AI curiosity and into real, deployable capability. Participants build practical skills across modern AI fundamentals, RAG (vector, graph, and hybrid), agents (single to multi-agent), MCP-based tool integration, and security patterns that reflect how enterprise AI actually fails and succeeds.

The program culminates in a capstone where each participant ships an “enterprise-shaped” AI customer support chatbot: RAG grounded in organizational data, an AI agent that can use tools, an MCP server + client for structured integration, a Gradio UI with user/operator views, persistence and demo seed data, and a deployment to Hugging Face Spaces using secrets—plus observability basics and test queries. The curriculum is customizable for your organization, and optional modules can be swapped in for deeper focus on tools like GitHub Copilot, Claude Code, Codex, and more.

See details on our standard 5-day agenda below.

 Day 1 is about building shared language and practical clarity—fast. We cover the current state of AI in 2026 (agents, models, hardware, and real enterprise use cases), the difference between ML, deep learning, and generative AI, and what “inference” means for cost, latency, and capability. Then we connect it to the enterprise stack with adoption patterns, practices, and pitfalls teams keep repeating.

We also get hands-on with prompt engineering fundamentals that translate into better reliability: role/structured/constrained patterns, few-shot examples, and how to measure prompt effectiveness. Finally, we talk about staying relevant without burning cycles—how to build an update strategy and use AI to automate research and internal enablement.

 Day 2 takes participants from “how models work” to “how to extend them responsibly.” We unpack the essentials—embeddings, transformers, attention, parameters, and the modern model landscape (frontier, reasoning-focused, and open source). Then we shift to practical model qualification and hands-on fine-tuning for business contexts.

The second half is a deep dive into RAG as an enterprise capability: why models alone aren’t enough, how vector databases and pipelines actually behave, and when graph RAG or hybrid retrieval makes sense. Participants build complete RAG apps and learn quality techniques like re-ranking and corrective RAG (CRAG) to reduce hallucinations and improve grounded answers.

 Day 3 starts with the leap from chatbots to agents that can plan, use tools, and do useful work. We cover what agents are (and what they aren’t), why they matter in enterprise contexts, and how to design them for reliability—reasoning approach, memory, state, and data management.

Then we get hands-on: tool-using agents, multi-agent patterns, and agentic workflows that incorporate RAG for grounded decision-making. The focus is practical design patterns your teams can reuse—so “agentic” doesn’t become a fragile demo that falls apart under real inputs.

 In the second half of Day 3, we build real integration capability with the Model Context Protocol (MCP). Participants learn what MCP is, how it works, and why it’s emerging as a standard approach for connecting AI agents to tools and systems with clearer structure and safer boundaries.

You’ll build an example corporate MCP server, learn transport and framework considerations, and implement structured integration practices like classification and canonical queries. Then you’ll connect MCP to RAG to create repeatable, enterprise-friendly patterns for “AI that can do things” without turning your architecture into a tangle of one-off tool calls.

Day 4 is security-focused, and it’s designed for engineering reality—not checkbox theory. We start with RAG threats like document poisoning and how to defend against them using practical mitigations and validation approaches.

Then we tackle agent security and control: managing budgets, designing safe multi-agent interactions, resisting prompt injection, and implementing defense-in-depth patterns. Finally, we harden MCP interactions with authentication and authorization, per-tool scopes, rate limiting, input validation, and output sanitization—so your AI applications behave like enterprise software, not a brittle prototype.

 The capstone is where it all comes together. Participants assemble an end-to-end AI support chatbot with RAG + MCP tools, a Gradio web UI with user/operator views, persistence (SQLite) and seeded demo data, and observability basics like logs, dashboards, and test queries.

You’ll build an MVP first, then iterate toward a fuller feature set: advanced RAG, more capable MCP tooling, a stronger UI, and a deployment to Hugging Face Spaces using secrets (no hardcoded tokens). The goal is a product you can demo, extend, and use as a blueprint back at work.

Day 5 answers the question most teams hit after their first successful demo: how do you get from 80% working to production? We do a quick refresh on patterns and security essentials, then focus on the “last mile”—reliability, testing, observability, integration constraints, rollout strategies, and operational readiness.

We also cover AI-assisted SDLC practices: using AI to build, deploy, monitor, maintain, and document systems—without letting it degrade quality. And we close with a practical approach to incremental adoption: deciding where AI belongs, where it doesn’t, and how to add AI features safely over time.