AI for Product Builders

Set up a Python environment with virtualenv, API keys, and the OpenAI and Anthropic SDKs.

Make a first chat completion API call and inspect the response object.

Use system, user, and assistant message roles to shape conversational behavior.

Control output with temperature, top_p, max_tokens, and stop sequences.

Stream responses token-by-token and explain why streaming improves UX.

Handle API errors, rate limits, and retries with exponential backoff.

Track token usage and estimate the cost of a request programmatically.

Write clear, specific prompts using role, task, context, and format instructions.

Apply few-shot prompting with examples to steer model behavior.

Use chain-of-thought and step-by-step reasoning prompts for complex tasks.

Decompose a complex task into a chain of smaller prompts.

Diagnose and iteratively fix a failing prompt using systematic A/B comparison.

Recognize and mitigate common prompt failure modes (ambiguity, leading questions, contradictions).

Force JSON outputs using JSON mode and validate them with Pydantic schemas.

Define and call functions/tools via the OpenAI function-calling API.

Design tool schemas that an LLM can reliably invoke.

Implement a web-search tool using a third-party API and have the LLM call it to answer a question about a recent event.

Use Pydantic validation feedback to construct a corrective re-prompt when an LLM output is malformed.

Compare function calling vs. structured output modes and pick the right one per task.

Explain what a text embedding is and why semantic similarity matters for products.

Generate embeddings using an embeddings API and inspect their dimensions.

Compute cosine similarity between embeddings in Python.

Store and query embeddings in a vector database (e.g., Chroma or Pinecone).

Build a simple semantic search over a small document collection.

Choose between embedding models based on cost, dimension, and quality trade-offs.

Explain the RAG architecture and decide when it outperforms fine-tuning or simply using a larger context window.

Chunk documents effectively (size, overlap, semantic boundaries) for retrieval.

Implement the ingestion stage of a RAG pipeline: chunk source documents, embed them, and store them in a vector database.

Implement the retrieval stage: accept a user query and fetch the most relevant document chunks.

Implement the generation stage: assemble a prompt with retrieved context and produce a synthesized answer.

Use metadata filtering to scope retrieval to relevant subsets of documents.

Improve retrieval quality using advanced techniques like hybrid search and re-ranking.

Diagnose and mitigate common RAG failure modes (e.g., incorrect chunking, irrelevant retrieval, poor synthesis).

Define an LLM agent, distinguish it from a single prompt or RAG pipeline, and explain the ReAct (Reason → Act → Observe) loop.

Design a multi-step task for an agent and implement the Python functions for two distinct tools it will use.

Implement a single Reason → Act → Observe cycle that selects a tool, executes it, and formats the observation.

Assemble the full agentic loop that repeats until the task is complete.

Compare agent frameworks (LangChain, LlamaIndex, raw API) for an indie-hacker workflow.

Implement short-term memory in an agent using a conversation buffer.

Implement persistent memory in an agent using conversation summaries stored in a vector database.

Decide when a deterministic workflow beats an autonomous agent for a product feature.

Define evaluation criteria (accuracy, helpfulness, tone, safety) for an LLM feature.

Build a small golden dataset of inputs and expected outputs for regression testing.

Use LLM-as-a-judge to score outputs automatically and recognize its biases.

Run prompt experiments and compare versions using a tool like Promptfoo or a simple script.

Set up basic logging and tracing of LLM calls (e.g., with Langfuse or LangSmith).

Use real user feedback (thumbs up/down, edits) to continuously improve prompts.

Identify common AI UX patterns (chat, inline suggestion, command bar, generative canvas).

Write clear, non-technical in-app copy and disclaimers that explain AI limitations (hallucinations, knowledge cutoffs).

Select UI patterns (beta tags, confidence scores, source citations) that signal AI-generated content and its reliability.

Use streaming, skeleton loaders, and progress indicators to mask latency in AI interactions.

Design effective error and ‘I don’t know’ states for LLM features.

Design a user flow that lets users verify, edit, or report AI-generated outputs.

Design onboarding patterns that teach users how to prompt your AI feature effectively.

Measure the latency of an LLM call and identify its main components (network, time-to-first-token, generation time).

Reduce latency through model choice, prompt size tuning, and parallel API calls for batch tasks.

Implement a prompt cache (key-value lookup) to cut cost on identical requests.

Implement a semantic cache using embeddings and vector similarity for paraphrased requests.

Compare hosted APIs, self-hosted open-source models, and fine-tuning, and build a per-request cost model to choose a strategy.

Mitigate prompt injection with defensive prompting and input validation, and use a moderation API to filter unsafe content.

Detect and redact PII before sending data to an LLM, and implement PII-aware logging.

Recognize common agent failure modes (loops, runaway cost, bad tool choices) and add guardrails such as turn limits and cost budgets.

Create a basic FastAPI endpoint that accepts a POST request and calls your LLM pipeline.

Add API key authentication to the FastAPI backend.

Implement per-user rate limiting on the FastAPI backend to control cost and abuse.

Choose between Streamlit and a JavaScript framework based on time-to-market, and build a Streamlit frontend that calls your backend.

Deploy the FastAPI backend to a serverless platform (e.g., Railway or Fly.io) and the frontend to Vercel, connecting them end-to-end.

Implement a human-in-the-loop confirmation flow in the frontend for high-stakes AI actions.

Integrate a Stripe subscription checkout flow and a webhook handler that updates a user’s subscription status.

Integrate a product analytics tool (e.g., PostHog) and instrument events for sign-up, activation, and core AI feature usage.

Evaluate an LLM product idea against the ‘thin wrapper’ risk and defensibility criteria.

Identify durable moats for AI products (data, workflow, distribution, UX, integrations).

Define a framework for scoping an AI MVP (magic moment, user journey, minimal feature set).

Apply the MVP scoping framework to a candidate product idea and estimate its build timeline.

Price an AI product accounting for variable token costs and target gross margin.

Plan a launch strategy for an indie AI product (Product Hunt, communities, content).

Sketch how a product roadmap should evolve as model capabilities and costs change over the next 12 months.