IBM watsonx Generative AI Engineer

Using a model well starts with knowing its limits. You study what large models do reliably, where they fail, and the risks that follow for accuracy, privacy, bias and intellectual property.

• Strengths: language, summarization and code
• Hallucination and unsupported claims
• Knowledge cutoffs and stale information
• Context length and what is lost
• Bias, toxicity and unsafe output
• Privacy and intellectual property risks
• Evaluating whether a model fits a task

You build A risk register for a proposed language-model feature.

Prompts are the cheapest way to change what a model does. You write clear instructions, add examples, control decoding settings, and iterate against test cases instead of impressions.

• Instructions, roles and structure
• Zero-shot and few-shot prompting
• Step-by-step reasoning prompts
• Temperature, top-p and length controls
• Prompt templates with variables
• Resisting prompt injection
• Iterating against a set of test inputs

You build A tested prompt template with measured gains over a first draft.

When a good prompt is not enough, you can train a small set of prompt parameters while leaving the model untouched. You prepare tuning data, run a tuning job, and judge whether it beats careful prompting.

• Soft prompts against hand-written prompts
• When prompt tuning is worth it
• Preparing input and output training examples
• Running a tuning experiment in watsonx.ai
• Reading loss and evaluation results
• Comparing a tuned prompt with an engineered one

You build A prompt-tuned model compared against a carefully engineered prompt on one task.

Full fine-tuning is expensive and risky. You learn how low-rank adaptation updates a small share of parameters, what quantization adds, and how to choose between tuning approaches for a given budget and goal.

• Full against parameter-efficient fine-tuning
• Low-rank adaptation explained
• Quantized fine-tuning in outline
• Catastrophic forgetting and how to limit it
• How much data a tuning job needs
• Choosing prompting, tuning or fine-tuning

You build A decision guide that picks a tuning approach for four scenarios.

InstructLab lets you add skills and knowledge from a small set of examples. You write taxonomy entries with seed questions and answers, generate synthetic training data, train and test the result.

• The taxonomy of skills and knowledge
• Writing seed examples that teach well
• Generating synthetic data with a teacher model
• Training and serving the tuned model
• Testing what the model learned and what it lost
• Reviewing synthetic data for quality problems

You build A taxonomy contribution that adds one new skill to a model, tested before and after.

Retrieval depends on turning text into vectors and searching them well. You split documents sensibly, create embeddings, measure similarity, and choose and query a vector store.

• What an embedding captures
• Chunking strategies and chunk size
• Cosine similarity and other distance measures
• Vector stores and approximate search indexes
• Metadata and filtering
• Choosing an embedding model

You build A searchable vector index over a real set of documents.

Retrieval grounds a model in your own documents without retraining it. You build the full pipeline from ingestion to generation, improve results with reranking, and measure whether the answers are faithful to their sources.

• The pipeline: ingest, chunk, embed, retrieve, generate
• Building the prompt from retrieved passages
• Reranking and hybrid search
• Citing sources in answers
• Measuring faithfulness and relevance
• Retrieval against fine-tuning for new knowledge
• Common failure modes

You build A retrieval-augmented question-answering service with an evaluation set.

LangChain assembles models, prompts, retrievers and tools into applications. You compose chains, connect them to watsonx.ai models, add retrieval and memory, and keep the result testable.

• Models, prompts and output parsers
• Composing chains with the expression language
• Retrievers and document loaders
• Conversation memory
• Tools and simple agents
• Using watsonx.ai models inside LangChain
• Debugging a chain step by step

You build A LangChain application that answers questions over documents using a watsonx.ai model.

Production use means calling models from code and chaining steps reliably. You authenticate, call watsonx.ai through its APIs and SDK, connect it with other services, and orchestrate multi-step AI workflows.

• Authenticating with API keys and tokens
• Calling models through the REST API
• Using the Python SDK
• Projects, spaces and where assets live
• Connecting to other services
• Orchestrating multi-step AI workflows
• Handling rate limits and errors

You build A script that calls a deployed model and feeds its output into a second service.

A working notebook is not a deployment. You plan environments, size models against cost and latency, deploy to an endpoint, monitor behavior and manage versions so changes can be rolled back.

• Deployment options for prompts and tuned models
• Trading cost, latency and quality
• Creating and testing a deployment endpoint
• Monitoring usage and output quality
• Versioning prompts and models
• Guardrails on inputs and outputs

You build A deployed model endpoint with monitoring and a version history.

Solve one business problem end to end: choose the model, engineer and tune prompts, ground answers with retrieval, build the application in LangChain and deploy it on watsonx.ai, with evidence at each decision.

• Choosing a problem and a model
• Prompting and tuning for the task
• Adding retrieval over company documents
• Building and deploying the application
• Evaluating and documenting the result

You build A deployed generative AI application with an evaluation record.