Paste a prompt that gives weak results and get a diagnosis of its flaws, a rewritten and structured version, a change log and test cases to check it really works better.
Design a retrieval-augmented generation chatbot: ingestion and chunking, embeddings, hybrid search, citations, permissions, evaluation, cost and risks such as prompt injection.
Get an adversarial test plan for your chatbot or AI assistant: prompt injection, data leaks, out-of-scope answers and made-up promises, with a test case table and mitigations.
Design a prompt that sorts texts (tickets, reviews, emails) into your labels, with clear definitions, balanced examples, structured output and a plan to measure its accuracy.
Design an evaluation for AI answers with an anchored rubric, an LLM-as-judge prompt with JSON output, bias mitigation and calibration against human labels.
Build a reliable pipeline to extract free text into JSON with an LLM: JSON Schema, a prompt with null rules, few-shot examples, validation code with retries and test cases.