Gen AI Engineer
Become a Generative AI engineer — from how LLMs work, through building with model APIs, embeddings, and vector stores, into retrieval and agents, then fine-tuning and shipping evaluated systems to production.
Getting Started
The Gen AI Engineer Role
What a Generative AI engineer does: composing pre-trained foundation models into real products rather than training models from scratch.AI vs ML Engineer
How the role differs from a traditional machine-learning engineer.How LLMs Work
Next-token prediction, the transformer stack, and why capability emerges with scale — plus the limits (hallucination, stale knowledge) to design for.Transformers
The layered self-attention architecture behind modern LLMs.Attention
How every token weighs every other token to build context.
Foundations
Prompt Engineering
Steer behaviour with the input alone: clear instructions, few-shot examples, structured output, and reasoning techniques — plus defending against prompt injection.Few-shot Prompting
Guiding the model with a handful of worked examples in the prompt.Chain-of-Thought
Prompting the model to reason step by step before answering.Tokens & Context Windows
Text is consumed as tokens, and every model has a finite context window. Manage cost, latency, and long inputs with chunking and summarization.Context Window
The fixed token budget a model can attend to at once.
Building with LLMs
Model APIs & SDKs
Call hosted models: chat completions, streaming, tool calling, and structured outputs, while handling rate limits, retries, and cost — and staying portable across providers.Streaming
Streaming tokens as they are generated for responsive UIs.Function Calling
Letting the model return structured calls to your own tools.Embeddings
Turn text into dense vectors where semantic similarity becomes distance — the foundation for search, clustering, and retrieval.Similarity Search
Finding the nearest vectors to a query embedding.Vector Databases
Store and search embeddings at scale with approximate nearest-neighbour indexes, metadata filtering, and hybrid search. From here the path forks.Hybrid Search
Combining keyword and vector search for better recall.
Application Patterns
- Retrieval
Retrieval-Augmented Generation
Ground answers in your own knowledge base: chunk and embed documents, retrieve the relevant passages at query time, and condition generation on them to cut hallucination. - Agents
AI Agents & Tool Use
Let a model plan and act: give it tools, let it decide which to call, observe results, and loop until a goal is met — with guardrails that keep autonomous loops bounded.
- Retrieval
Customization
Fine-tuning & Adaptation
Both tracks rejoin here. When prompting and retrieval are not enough, adapt the model itself: supervised fine-tuning, LoRA, and preference alignment such as RLHF or DPO.LoRA / PEFT
Parameter-efficient fine-tuning that trains only small adapters.RLHF / DPO
Aligning a model to human preferences after fine-tuning.Multimodal AI
Go beyond text: vision-language models, image generation, and speech-to-text / text-to-speech, combined in a single application.Vision
Models that understand and generate images.Speech
Transcribing and synthesizing audio.
Production
Evaluation & Guardrails
You cannot ship what you cannot measure. Build evals for quality, faithfulness, and safety with automated scorers, LLM-as-judge, and human review, then add guardrails.Guardrails
Input/output filtering, moderation, and grounding checks.Deployment & Monitoring
Ship the system and keep it healthy: manage latency and cost, cache and stream responses, and monitor usage, drift, and safety in production.Monitoring
Tracking latency, cost, drift, and safety signals live.