Corporate Training · Generative AI

Generative AI Corporate Training: LLMs, RAG and Production-Ready Applications

Hands-on training in generative AI, large language models and retrieval-augmented generation (RAG) for engineering teams, data teams and the leaders who approve their work. We design each programme around your stack and your use cases, and every module includes a lab. By the end, your team can build, evaluate and secure an LLM application that runs on your own data, rather than a demo that falls apart the first time a real user asks a question.

We deliver onsite, live virtual or hybrid, in your time zone, anywhere in the world.

Scope a programme →

Audience

Who this programme is for

L&D and HR heads who need a GenAI programme that covers roles from executives to engineers, with an assessment and a report at the end.
Engineering managers and CTOs whose teams have to move from proof of concept to something they can run and support.
Data, ML and platform engineers who will own retrieval pipelines, vector stores, evaluation and deployment.
Database teams whose documents and records already sit in Oracle and who want to add semantic search without adding another system.
Universities that need a semester module in applied generative AI.

The problem

Why GenAI projects stall

The models are usually not the hard part. Most teams run into the same handful of problems:

The pilot never reaches production

A chatbot that works on ten hand-picked questions falls over when it meets real documents, real users and real access rules.

Nobody can say whether it works

Without an evaluation set, teams judge answers by eye, and every change to a prompt or a chunk size becomes a debate.

Confident, wrong answers

If retrieval returns the wrong passages, the model answers fluently from them anyway, and users stop trusting the tool.

Security and data questions go unanswered

Where does the data go? Can a user see documents they shouldn’t? What happens when someone pastes an instruction into a document that the system later retrieves?

Costs are hard to predict

Token usage, embedding volumes and vector storage all scale differently, and the first real bill often comes as a surprise.

Leaders and engineers don’t share a vocabulary

Decision-makers can’t judge proposals, and engineers who learned from tutorials have seldom covered evaluation, guardrails or operations.

The programme addresses each of these directly.

Curriculum

Programme modules

Each module is a building block. We choose and order the modules for your audience after the scoping call, and we write the labs against your data or a close equivalent.

1. LLM fundamentals for practitioners

Skills: tokens, embeddings and context windows; how temperature and sampling affect output; choosing a model for quality, latency and cost; estimating monthly spend before you build.

Lab: Call two foundation models through the Amazon Bedrock Converse API from Python, run the same task on both, then compare quality, token counts, latency and cost.

2. Prompt engineering that holds up

Skills: system prompts, few-shot examples, structured (JSON) output, prompt chaining, and keeping prompts under version control instead of in someone’s notebook.

Lab: Build a small test set for a real task from your team, iterate on the prompt against it, and record which change fixed which failure.

3. RAG with vector databases

Skills: chunking strategies, choosing an embedding model, similarity metrics, hybrid (keyword plus vector) search, metadata filtering and reranking. You’ll also learn when RAG is the right answer and when it isn’t.

Lab: Build a RAG pipeline in Python over a document set, including loading, chunking, embedding, storing and retrieving. Then change one variable at a time and watch how the answers change.

4. Building GenAI applications on AWS

Skills: Amazon Bedrock Knowledge Bases, and how to choose a vector store for them (Amazon OpenSearch Serverless, Amazon Aurora PostgreSQL with pgvector, Amazon S3 Vectors); IAM and private connectivity for Bedrock; Amazon Bedrock Agents and AgentCore for tool-using agents; Amazon SageMaker AI for teams that need to host or customise their own models.

Lab: Create a Bedrock knowledge base over documents in Amazon S3, query it, and compare the results with the pipeline you built by hand in Module 3.

5. Vector search inside Oracle Database 23ai / 26ai

Skills: the VECTOR data type; loading ONNX embedding models into the database and generating embeddings with VECTOR_EMBEDDING; similarity search with VECTOR_DISTANCE; HNSW and IVF vector indexes; combining relational filters and semantic search in a single SQL statement.

Lab: Run an Oracle Database container, load an embedding model, vectorise a table of documents, and build a retrieval query that also enforces business filters.

6. Evaluating LLM and RAG applications

Skills: building a golden question set; measuring retrieval (context relevance and coverage) separately from generation (faithfulness, correctness, completeness); LLM-as-a-judge and where it goes wrong; RAG evaluation jobs in Amazon Bedrock; when you still need human review.

Lab: Write an evaluation set for your use case, score two retrieval configurations, and choose one based on the numbers rather than opinion.

7. Guardrails, security and responsible use

Skills: prompt injection (direct and through retrieved documents), data leakage, PII handling, applying document-level access control to retrieval, logging and audit. You’ll configure Amazon Bedrock Guardrails: denied topics, sensitive information filters, prompt attack filters and contextual grounding checks.

Lab: Red-team your own application, add a guardrail and access controls, then run the same attacks again and compare.

8. LLMOps: running it in production

Skills: packaging with Docker, deploying on AWS, infrastructure as code with Terraform, gating prompt and model changes on evaluation results in CI, and monitoring tokens, latency, cost and quality over time.

Lab: Deploy the RAG application with Terraform, add an evaluation step that blocks a regression, and set up the metrics you would watch in week one.

Tracks

Tracks by audience

The durations below are starting points. We fix the final length after scoping.

Track	Who it’s for	Modules	Duration options
GenAI for leaders	Executives, product owners, L&D and business heads	What LLMs and RAG can and can’t do, use-case selection, cost, risk and governance, how to judge a GenAI proposal. No code.	Half-day or 1-day workshop
GenAI for developers	Software engineers with Python basics	1, 2, 3, 4, 6, 7	3-day intensive, or 30 hours over 5 weeks (live virtual)
RAG and LLMOps for data and ML engineers	Data, ML and platform engineers	All eight modules, in more depth on 3, 6, 7 and 8	5-day intensive, or 6–8 weeks part-time
Vector search for Oracle teams	Oracle DBAs and PL/SQL developers	1, 3, 5, 6	2 days
Academic module	University faculty and students	Selected modules, mapped to your syllabus and assessment scheme	One semester

Formats

Delivery formats

Onsite

Our trainer comes to your office, in India or internationally. This works best for intensive cohorts and workshop-heavy content.

Live virtual

Instructor-led online sessions scheduled for your time zone, with lab environments, recordings and support after each session.

Hybrid

Live workshops combined with self-paced modules and checkpoints, which suits distributed teams and longer programmes.

Short burst or multi-week

A 1–3 day masterclass on one topic, or a multi-week programme with labs between sessions, following the same model we have used for multi-week bootcamps.

All delivery is in English, for teams of five or five hundred.

Process

How an engagement runs

1. Scoping call

We cover your stack, your team’s current level, the business reason for the training and the time available.

2. Custom curriculum

We write the agenda, labs and assessments for your systems and use cases, and you review them before delivery.

3. Delivery

Onsite, live virtual or blended, in your time zone.

4. Hands-on labs

These run on your cloud accounts or in an environment we provide. Every session is hands-on, not slides only.

5. Assessment and report

We measure before and after so you can see the change, and issue completion certificates and a summary for the sponsor.

6. Follow-up

A support window after delivery, for real questions that come up in real work.

If you also need the application built, our AI and LLM consulting team can deliver it and then train your engineers to run it.

Proof

The experience behind the programme

17 yearsin industry

3,000+engineers trained

40+enterprise clients

12+countries

100%positive training feedback

PITC is led by founder Pushpjeet Cholkar, who leads every engagement from start to finish. He has delivered training and consulting for enterprises and bootcamp-style programmes internationally, including work with NeueFische Germany. That experience covers live virtual delivery, multi-week cohorts and curriculum built for practising engineers rather than slide decks.

Certified on the platforms we teach: AWS Certified Solutions Architect – Associate; Oracle Cloud Infrastructure Foundations 2020 Associate; Oracle Certified Professional 11g DBA; OCP Advanced PL/SQL; OCP Developer 9i, 10g and 11g.
We build what we teach: RAG systems over enterprise documents are part of our consulting practice, and RAG-based tutoring systems are the founder’s active research area.
We teach at university level: our faculty teaches semester modules at Parul University and ITM University, and delivers curriculum for international bootcamps.

Client references are available on request for shortlisted engagements, subject to our clients’ confidentiality terms.

Pre-work

Free resources to start with

If your team is new to the topic, these free courses make good pre-work before a live programme:

AI Foundations

13 short, illustrated lessons from “what is AI?” to transformers, attention and how LLMs work, with a quiz after each lesson. About 2.6 hours, self-paced.

AI & ML Foundation Course

The course overview, lesson list and audience guide, including a track for managers who need to judge AI proposals.

AWS Cloud Foundations

Eleven modules on cloud, IAM, VPC and core AWS services, useful before the Bedrock labs.

FAQ

Frequently asked questions

What is generative AI corporate training?

Generative AI corporate training is an instructor-led programme that teaches a company’s staff to use and build with large language models. At PITC, that covers LLM fundamentals, prompt engineering, retrieval-augmented generation (RAG), building on Amazon Bedrock, evaluation, guardrails and running applications in production, with hands-on labs built around your own stack and use cases.

Who should attend?

We run separate tracks for leaders and decision-makers (no code), software developers, data and ML engineers, and Oracle database teams. Mixing these audiences in one room usually serves nobody well, so we recommend a short leadership session alongside a deeper engineering track.

How long is the training?

It depends on the audience and the depth you need. Leadership workshops typically run half a day to a day. Engineering tracks run from a 1–3 day masterclass to a multi-week part-time programme. We agree the final duration after a scoping call.

Do participants need prior experience?

The leadership track needs none. Developer and engineering tracks assume basic Python. For anyone who needs a refresher, our free AI Foundations course covers the concepts behind LLMs and can be set as pre-work.

Can the labs run on our own AWS account?

Yes. Labs can run on your cloud accounts, so your team learns inside the same permissions, networking and data rules they’ll face in production. If that isn’t practical, we provide the lab environment.

Is the training only about Amazon Bedrock?

No. The concepts (prompting, retrieval, evaluation, security, operations) apply to any platform. Our labs are built on AWS, including Bedrock, SageMaker AI and several vector stores, and on Oracle Database AI Vector Search for teams whose data already lives in Oracle. Tell us your stack during scoping.

How do you measure whether the training worked?

We assess participants before and after the programme so you can see how their skills changed. Participants receive completion certificates, and the sponsor receives a summary report. A support window after delivery covers questions that come up when the team applies the material.

Can you also help build our GenAI application?

Yes. Our consulting practice designs and delivers RAG and LLM systems on enterprise data, including evaluation and deployment. Many clients combine the two: we build the first system with their team, then train the engineers to run and extend it.

Plan your team’s programme

Tell us the audience, the team size, your stack and your preferred dates. We’ll send back an agenda and a quote.