Production AI Engineering Training

RAG (Retrieval-Augmented Generation) connects an LLM to approved company data such as documents, databases, and knowledge bases. Retrieved evidence can make answers more current and easier to verify, but RAG does not eliminate hallucinations; retrieval quality, evaluation, access controls, and monitoring still matter.

RAG retrieves relevant information at request time without changing the model's weights. Fine-tuning changes model behaviour by training on examples. The right choice depends on the use case: RAG is often suitable when knowledge changes frequently or answers need source evidence, while fine-tuning can help with specialised behaviour, style, or task performance. Some systems use both.

You do not need deep data-science Python, pandas mastery, TensorFlow or PyTorch before joining. You need strong intermediate application Python: functions, classes, type hints, APIs, async basics, FastAPI, SQL, Git, testing and error handling.

Start with the AI Programming with Python bridge course. It teaches the backend Python skills needed before RAG, agents, LangGraph, MCP and production AI projects.

LangChain provides components for composing prompts, retrievers, models, and tools. LangGraph adds stateful graph execution for workflows that loop, branch, retry, pause for human approval, or preserve state across multiple steps. Teams should select a framework based on workflow complexity, operational requirements, and existing architecture.

Model Context Protocol (MCP) is an open protocol for connecting compatible AI clients to tools and data sources through a consistent interface. The programme covers how an MCP server exposes tools, how clients discover them, and how to design integrations with appropriate authentication, permissions, and error handling.

Yes. A pre-training discovery call identifies your cloud platform, databases, internal tools, and team experience. Where practical, project examples can be adapted to AWS, Azure, or GCP services while preserving the same architecture and evaluation principles.

The 5-day intensive is suited to teams that can dedicate one working week to training. The 8-week format provides more time for practice between sessions and may fit teams with ongoing delivery commitments. Both formats cover the same core curriculum.

Participants build a PDF question-answering application, a resume search engine, a multi-document company knowledge assistant, a YouTube transcript assistant, and a multi-agent workflow capstone. Projects are adapted to the delivery format and the team's approved technology environment.

Eight to sixteen participants. Small enough for the trainer to debug code live for each team; large enough for diverse problem-solving. For larger teams, we run parallel batches.

Unlike a prerecorded course, this programme includes live instruction, code review, architecture discussion, and exercises adapted to the team's context. Participants receive feedback while building the projects instead of relying only on fixed tutorial solutions.

Yes. Governance is applied across the RAG, agent, tool-calling, MCP, and deployment modules. Teams practise system ownership and risk classification, data and access boundaries, prompt-injection and sensitive-data testing, least-privilege permissions, human approval, evaluation, audit logging, monitoring, incident response, and rollback.

No. The training is informed by recognised AI risk and application-security practices, but it does not provide ISO certification, a legal opinion, regulatory approval, or assurance that a specific system is compliant. Each organisation must complete its own security, privacy, legal, and risk review with qualified teams or advisers before production use.