Prompts

RAG Production Architecture

Design a production-grade RAG system architecture covering scaling, reliability, observability, cost management, and operational runbooks.

RAG System Evaluation

Build a comprehensive evaluation framework for RAG systems measuring retrieval quality, generation accuracy, and end-to-end performance.

RAG Embedding Pipeline

Design a scalable embedding pipeline for RAG systems covering model selection, batch processing, storage, and incremental updates.

RAG Retrieval Architecture

Architect a robust retrieval layer for RAG systems including hybrid search, re-ranking, filtering, and query transformation strategies.

RAG Document Chunking Strategy

Design an optimal document chunking strategy for Retrieval-Augmented Generation systems, balancing chunk size, overlap, and semantic coherence.

LLM Observability & Debugging

Design observability systems for LLM applications with request tracing, quality monitoring, and debugging workflows.

Multi-Model LLM Architecture

Design multi-model LLM architectures with intelligent routing, fallback chains, and cost-quality optimization across providers.

LLM Fine-Tuning Data Preparation

Design data preparation pipelines for LLM fine-tuning with quality filtering, formatting, and evaluation dataset creation.

LLM Response Caching

Design LLM response caching systems with semantic similarity matching, cache invalidation, and cost optimization strategies.

LLM Evaluation Framework

Design comprehensive LLM evaluation frameworks with automated metrics, human evaluation, and regression testing for quality.

LLM Chain & Agent Design

Design LLM chain and agent architectures with multi-step reasoning, tool use, and state management for complex workflows.

LLM Output Parsing & Validation

Design robust LLM output parsing systems with structured extraction, validation, retry logic, and graceful degradation.

LLM Function/Tool Calling

Design LLM function calling architectures with tool definitions, execution safety, and error handling for agentic workflows.

LLM Context Window Management

Design context window management strategies for LLMs with efficient token usage, context compression, and retrieval augmentation.

LLM Prompt Architecture

Design systematic prompt engineering architectures with template management, version control, and evaluation frameworks.

AI Incident Response Plan

Design incident response plans for AI system failures covering detection, containment, communication, and post-incident review.

AI Transparency & Explainability

Design AI transparency and explainability systems with interpretable outputs, audit trails, and stakeholder explanations.

Human-in-the-Loop AI Design

Design human-in-the-loop AI systems with appropriate automation levels, escalation paths, and human oversight for decisions.

AI Bias Audit Framework

Design a bias audit framework for AI systems covering detection, measurement, mitigation, and ongoing monitoring across demographics.

AI Red Team Testing

Design AI red teaming frameworks for systematically discovering vulnerabilities, biases, and failure modes in AI systems.

AI Safety Guardrails Design

Design safety guardrail systems for AI applications with input validation, output filtering, and behavioral boundaries.

ML Model Retraining Strategy

Design model retraining strategies with trigger-based scheduling, data management, and validation for continuous model freshness.

Production ML Monitoring

Design comprehensive production monitoring for ML systems covering model health, infrastructure, and business impact metrics.

ML Model Governance

Design ML model governance covering model inventory, risk assessment, approval workflows, and regulatory compliance.