Business Analyst to AI Engineer

Classify a business opportunity as predictive analytics, generative AI, retrieval, tool-based automation, or a non-AI solution.

Translate an ecommerce, sales, marketing, or finance pain point into a precise AI use-case statement.

Define a measurable success criterion for an AI use case.

Identify the data inputs required by a proposed AI solution.

Assess an AI use case with a feasibility-versus-value matrix.

Draw a data-flow diagram for an AI application.

Specify human oversight for a business decision supported by AI.

Diagnose missing values, outliers, and skewed variables in a business dataset.

Distinguish correlation from evidence of causation in a marketing analysis.

Estimate a bootstrap confidence interval for a business metric in Python.

Split time-dependent ecommerce data without introducing target leakage.

Build a reusable preprocessing pipeline with scikit-learn.

Fit a baseline regression model to a sales prediction problem.

Evaluate regression predictions using MAE and RMSE.

Compare a trained regression model against a naive business baseline.

Train a scikit-learn classifier for a customer or transaction outcome.

Interpret precision, recall, F1 score, and a confusion matrix for a business decision.

Set a classification threshold using the relative costs of false positives and false negatives.

Estimate model generalization with cross-validation.

Tune a small set of model hyperparameters without contaminating the test set.

Explain an individual prediction using feature attribution.

Perform model error analysis across business-relevant customer segments.

Document a predictive model's intended use and limitations in a model card.

Distinguish pretraining, instruction tuning, retrieval-augmented generation, and fine-tuning.

Explain how tokenization affects context usage and model inputs.

Trace the role of attention in a transformer inference pass at a conceptual level.

Run an open-weight instruction model in a local or hosted notebook environment.

Control a model response using decoding parameters.

Compare candidate language models on quality, latency, privacy, and cost.

Select prompting, retrieval, or fine-tuning for a given business requirement.

Separate persistent model instructions from task-specific user input.

Write a zero-shot prompt with explicit context, constraints, and output criteria.

Create a few-shot prompt from representative business examples.

Implement a bounded retry strategy for invalid model output.

Extract structured fields from an ecommerce or finance document with an LLM.

Create a repeatable prompt regression test set.

Generate text embeddings with an open embedding model.

Calculate cosine similarity between embedding vectors.

Assess whether an embedding model preserves business-relevant semantic similarity.

Choose a chunk size for a document collection using evidence from retrieval results.

Attach source and business metadata to document chunks.

Build a local vector index for business documents.

Retrieve relevant chunks with similarity search and metadata filters.

Measure retrieval performance with recall at k.

Implement a minimal retrieval-augmented generation pipeline.

Make a generated answer cite its retrieved sources.

Implement an evidence-based no-answer policy.

Rewrite an ambiguous user query for retrieval.

Rerank retrieved passages before generation.

Combine keyword and vector retrieval in a hybrid search pipeline.

Measure answer faithfulness against retrieved evidence.

Diagnose a RAG failure as an ingestion, retrieval, context, or generation problem.

Choose between a deterministic workflow and an autonomous agent for a business process.

Define a typed tool schema that a language model can invoke.

Execute a validated model-requested tool call.

Create a read-only SQL tool with restricted query scope.

Orchestrate a deterministic workflow containing an LLM step.

Preserve relevant state across a multi-turn interaction.

Implement an agent loop with explicit termination conditions.

Require human approval before an agent performs a consequential action.

Define an evaluation rubric for a business-facing AI response.

Implement deterministic checks for structured AI outputs.

Score open-ended model responses with a rubric-based evaluator.

Calibrate an automated evaluator against human judgments.

Trace an evaluation failure to a specific application component.

Compare application variants on quality, latency, and resource consumption.

Run an automated regression suite after a prompt, model, or retrieval change.

Design component boundaries for an AI service, retrieval layer, tools, and user interface.

Expose an AI workflow through a typed FastAPI endpoint.

Add token streaming to an AI application response.

Manage conversation state without placing sensitive data in prompts unnecessarily.

Add caching for a safe and repeatable AI operation.

Instrument an AI request with structured logs and traces.

Create a threat model for a business AI application.

Test an application for indirect prompt injection through retrieved content.

Apply an input or output control that limits a demonstrated prompt-injection attack.

Detect and redact personally identifiable information before model submission.

Configure application secrets using least-privilege AWS access.

Assess a model or dataset for licensing and privacy constraints.

Containerize an AI application for reproducible deployment.

Deploy a containerized AI application using a low-cost AWS architecture.

Write a one-page product brief for an ecommerce revenue intelligence assistant.

Build a representative capstone data package containing structured metrics and business documents.

Create an architecture diagram for the capstone application.

Implement the capstone document-ingestion and indexing pipeline.

Implement a restricted analytics tool over the capstone's structured data.

Combine retrieval and analytics tools in a controlled assistant workflow.

Evaluate the capstone against its acceptance criteria and threat model.

Publish a portfolio case study covering design choices, measured results, limitations, and next steps.