Pattern-Grounded Prompts

The system prompt that implements Basic RAG — grounds LLM responses in retrieved context and prevents hallucination when no context is found.

The system prompt that requires inline citations for every claim, detects insufficient context, and flags uncertainty to build trust in RAG outputs.

The two-phase prompt that separates strategic planning from execution — one phase creates a structured plan, another executes each step.

The system prompt that implements the ReAct loop — interleaving Thought, Action, and Observation steps so the agent reasons before acting and learns from results.

The zero-shot and few-shot chain-of-thought prompt templates that improve accuracy on multi-step reasoning tasks by making the model show its work.

The meta-prompt that generates correctly formatted few-shot examples for any classification, extraction, or generation task.

The prompt that generates multiple independent reasoning paths and aggregates to the most consistent answer, reducing errors from stochastic generation.

The prompt that classifies user intent and routes queries to the correct handler, tool, or agent pipeline without relying on keyword matching.

The input safety check prompt that screens user messages for policy violations, prompt injection attempts, and out-of-scope requests before passing to the main model.

The evaluation prompt that uses an LLM to score AI outputs on accuracy, groundedness, and relevance using a structured rubric, replacing manual review at scale.