Prompts
The hard parts of building a RAG pipeline aren't the ones tutorials cover
Most RAG and agent tutorials cover the happy path. The prompts that matter are for the decisions that actually determine whether the thing works in production: chunking strategy, retrieval tuning, guardrails, and how you evaluate it at all.
Last updated Aug 15 · 13 min read
Why this category exists: mid-build, not casual questions
The visitor to this category is mid-build on something real — an autonomous agent, a RAG pipeline, an internal support bot — not asking a chatbot a casual question. The AI Agents & RAG prompt library is written for that, with prompts scoped to genuine architectural decisions rather than beginner tutorials.
The RAG decisions that actually determine retrieval quality
Chunking strategy — how documents get split before embedding — is the single most underrated lever in RAG quality, covered by a dedicated chunking-strategy design prompt. Retrieval itself benefits from combining dense vector similarity with sparse keyword matching rather than dense alone — tuning hybrid retrieval covers exactly that combination. And picking the right embedding model and vector database is a real trade-off decision, not a default to accept unexamined.
Ingestion and multimodal content
A RAG ingestion pipeline metadata design prompt covers what metadata to preserve at ingestion time — provenance, source, access control — that becomes impossible to add back later. For documents with tables, images and charts embedded, multimodal PDF handling addresses content a naive text-only pipeline would silently lose.
Evaluation: the step most RAG builds skip entirely
A RAG system that has never been evaluated against a real test set is a system nobody actually knows the quality of — building an evaluation harness with RAGAS-style metrics is the prompt for closing that gap, and it belongs early in a build, not as an afterthought once something already feels broken.
Agent architecture: planning, memory, and multi-agent systems
Breaking a complex task into steps an agent can actually execute reliably is covered by task decomposition and planning. A multi-agent system with a planner coordinating executors — this architecture prompt — needs a genuinely different design than a single agent. And an agent that needs to remember things across sessions needs a deliberate memory architecture, not an ad-hoc solution bolted on later.
Safety: guardrails, sandboxing, and adversarial testing
A guardrail policy design prompt covers what an agent should never be allowed to do, stated explicitly rather than assumed. Code execution sandboxing addresses the real risk of letting an agent run generated code at all. And a prompt-injection red-team exercise tests whether untrusted content an agent processes can actually hijack its behaviour — worth doing before an adversary finds the gap for you.
Structured output, observability, and cost
Getting an agent to reliably return structured, schema-conformant output rather than loose prose is foundational for anything downstream that parses the result. An observability and tracing spec is what lets you actually debug an agent in production rather than guessing at what happened. And model routing for cost and latency matters the moment a build moves from prototype to something with a real bill attached.
MCP servers and voice agents
Designing a proper MCP server tool spec matters as more agent tooling standardises on that protocol. And a voice agent has a genuinely different design problem than a text one — turn-taking design addresses exactly that, plus a dedicated evaluation benchmark suite for measuring agent quality systematically rather than by vibes.
When these decisions need a real engineering team
A prompt structures your thinking through one decision. Building the actual production system — with the infrastructure, monitoring and iteration that a real RAG pipeline or agent needs — is genuine engineering work. That's exactly what Scult's AI agents team builds, or book a meeting to talk through your specific build.
Need this built into your business?
The free tools and prompts on this site handle the small, solved problems. If what you need is bigger — AI agents & automation, built and maintained for you — that's Scult's day job.
