Staying Up to Date With AI

Convert a raw product question into a supervised-learning dataset with explicit features and targets.

Build a scikit-learn preprocessing pipeline for numerical, categorical, and missing data.

Compare a regularized linear model with a tree-based model on the same validation scheme.

Use cross-validation to estimate how well a model will generalize.

Tune a small hyperparameter search without contaminating the test set.

Choose a classification threshold using product costs and model calibration.

Perform slice-based error analysis to identify where a model fails.

Expose a trained model through a typed local inference endpoint.

Implement a small neural-network training loop in PyTorch.

Trace how backpropagation computes parameter gradients through a multilayer network.

Compare the effects of learning rate, batch size, and regularization on training.

Diagnose vanishing, exploding, and unstable gradients from training signals.

Tokenize text and trace how token IDs become embedding vectors.

Calculate scaled dot-product attention for a small worked example.

Trace data through the main components of a transformer block.

Distinguish pretraining, instruction tuning, preference tuning, and inference.

Select an open model by comparing task fit, license, context length, size, and benchmark evidence.

Run a compact quantized language model locally or in a free hosted notebook.

Measure an open model's memory use, latency, throughput, and output quality on a fixed task.

Write an instruction prompt with explicit context, constraints, and acceptance criteria.

Use few-shot examples to correct a repeatable model failure.

Generate schema-constrained output and validate it in Python.

Estimate token usage and manage information within a model's context window.

Wrap local and hosted models behind a provider-independent Python interface.

Configure project context and coding conventions for an AI coding assistant.

Decompose a coding task into a specification, constraints, and testable acceptance criteria before prompting.

Use an AI coding assistant to implement a small change while keeping the developer in control of the design.

Generate tests with an AI assistant and verify that the tests can detect intentional defects.

Debug a failing program by supplying minimal reproducible evidence rather than broad repository access.

Review generated code for correctness, security, dependency, licensing, and secret-exposure risks.

Measure whether an AI-assisted workflow improves delivery time without increasing escaped defects.

Identify reachable customer segments without assuming prior domain expertise.

Map a customer's workflow to locate frequent, costly, or frustrating steps.

Write a problem-interview guide that avoids pitching and leading questions.

Distinguish strong problem evidence from compliments, feature requests, and hypothetical interest.

Rank candidate problems by urgency, frequency, economic impact, customer access, and competitive pressure.

Assess whether an AI approach has a defensible advantage over rules, search, or ordinary software.

Write a falsifiable problem, customer, and value hypothesis.

Design a low-cost demand test that asks for a meaningful commitment or payment.

Decide whether a knowledge-intensive task needs prompting, retrieval, fine-tuning, or a conventional search system.

Parse and chunk source documents while preserving useful metadata and provenance.

Create embeddings and retrieve semantically similar passages from a vector index.

Improve retrieval precision using metadata filters and hybrid lexical-semantic search.

Build a retrieval-and-generation pipeline that answers from supplied evidence.

Attach verifiable citations and refuse answers unsupported by retrieved evidence.

Measure retrieval quality separately from answer quality.

Improve a failing RAG pipeline with query rewriting, reranking, or chunking changes.

Choose between a deterministic workflow and an agentic loop for a given automation.

Define a typed tool schema that a language model can call reliably.

Implement a single-agent tool-execution loop with explicit termination conditions.

Persist workflow state so an interrupted automation can resume safely.

Add retries, timeouts, idempotency, and structured error handling to an AI workflow.

Place human approval gates around consequential or irreversible actions.

Test an agent for prompt injection, excessive permissions, and unintended tool use.

Build an automation that connects a model to one real external service in a sandbox.

Choose among prompting, RAG, parameter-efficient fine-tuning, and full fine-tuning for a model failure.

Create a small, versioned instruction dataset with consistent input and target formats.

Generate synthetic training examples and filter them with explicit human quality checks.

Configure a parameter-efficient fine-tuning run for a compact open model.

Compare a tuned model with its base model on a held-out task set.

Send image or document input to a multimodal model and request structured output.

Validate multimodal extraction results against source evidence.

Determine whether an adapted model's quality gain justifies its added operational cost.

Translate product requirements into explicit quality dimensions and pass criteria.

Construct a representative evaluation set containing normal, edge, and adversarial cases.

Implement deterministic and semantic graders for model outputs.

Use human review and model-based judging with clear rubrics and spot checks.

Compare two model variants with paired results and uncertainty estimates.

Trace a failed output to retrieval, prompting, model, tool, or data causes.

Test an AI system for prompt injection, data leakage, unsafe output, and tool abuse.

Create a regression gate and risk register for an AI system release.

Draw a production architecture showing model, data, retrieval, application, and observability boundaries.

Implement a versioned inference API with typed requests and responses.

Move a long-running AI task to an asynchronous job queue.

Profile latency and cost across the stages of an AI request.

Apply caching, batching, and model routing to meet a defined service budget.

Handle rate limits and transient failures with timeouts, retries, fallbacks, and idempotency.

Record traces, quality signals, latency, token usage, and cost without exposing sensitive data.

Containerize and deploy a small AI service to a low-cost environment.

Select a narrow beachhead customer and problem using collected evidence rather than technical novelty.

Conduct a problem interview and record observations without turning it into a product pitch.

Synthesize interview notes into pains, current alternatives, buying constraints, and disconfirming evidence.

Define a concierge or smoke test with a price and an observable commitment threshold.

Write a narrow MVP specification with non-goals, architecture, evaluation criteria, and operational limits.

Implement one end-to-end happy path for the MVP using previously tested components.

Run a pilot session and capture evidence of user value, reliability, and willingness to pay.

Create an evidence-based iteration plan and a monthly routine for evaluating important AI developments.