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LLM Training

LLM Training: Hands-On Programs with Large Language Models

Hands-on programs that teach GPT, Claude, Llama and Mistral in enterprise use-cases.

TL;DR

One-line answer: LLM training — teaches how to integrate large language models (GPT/Claude/Llama) into enterprise use-cases; includes prompt engineering, fine-tuning, evaluation and LLMOps.

  • Model selection: decision criteria across GPT-4, Claude, Llama, Mistral (capability × cost × data policy)
  • Hands-on prompt engineering, few-shot, structured output and function calling
  • Fine-tuning, RLHF/DPO, evaluation harnesses and production LLMOps observability

LLM Training — Programs

FAQ

Which LLM models are covered in the training?
Training is model-agnostic: GPT-4/GPT-4o (OpenAI), Claude Opus/Sonnet (Anthropic), Llama 3.x (Meta) and Mistral. We compare strengths, weaknesses, cost and data policy for each. Model recommendations are made based on your company's constraints (e.g. data residency).
Do you teach fine-tuning?
Yes, at intermediate and advanced levels. OpenAI fine-tuning API, LoRA/QLoRA for local Llama fine-tuning, and DPO (Direct Preference Optimization) examples. In most cases we recommend RAG + prompt engineering over fine-tuning; training teaches when to fine-tune.
Is prompt engineering training enough, or do I need to learn LLMs more deeply?
If you're in product or business, prompt engineering + use-case design is usually enough. If you're an engineer integrating LLMs into production, you also need model behavior, embeddings, tokenization, evaluation and LLMOps — covered in intermediate/advanced programs.
Can we use our own company's data in the training?
In corporate/private programs yes — labs run on your anonymized sample data. Public programs use standard datasets, but you can choose to work with your own data in individual projects. NDAs are signed before training when required.
What capabilities will I gain after LLM training?
Beginner: what LLMs are, what they do, when to use them, prompt engineering basics. Intermediate: API integration, RAG architecture, structured output, function calling. Advanced: Fine-tuning, evaluation, agent orchestration, LLMOps (cost, latency, drift, guardrails).