Imagine asking your policy system a simple question and getting a perfect answer in under a second. Now imagine asking it to analyse fifteen years of reports, submissions, evaluations, and operational metrics. One requires a chatbot. The other requires an engine.

Policy teams often reach for RAG because it looks neat and tidy. A chatbot that can trawl internal guidance, regulatory texts, consultation documents and memos feels like a small miracle. And to be fair, a well-tuned Retrieval-Augmented Generation system is a brilliant librarian. It fetches the right paragraph, summarises dense text, and saves you from spelunking through document drives that haven’t been organised since John Key was in office.

But the trouble begins when the questions evolve from “Where is this definition?” to “How does this policy interact with everything we’ve already said over the past decade?” That’s the moment top-N RAG quietly taps out.

Why top-N RAG chatbots look appealing

RAG takes your curated documents, turns them into embeddings, retrieves the most relevant ones for each query, and asks a large language model to produce an answer grounded in those snippets. For fast internal questions — the things analysts, advisors, and policy managers ask dozens of times a day — it’s perfect.

  • “Where is the cost-benefit guidance mentioned?”
  • “Which document defines emissions intensity?”
  • “What did we say in Version 3.1 versus 3.2?”

A quick, friendly, low-friction assistant. No politics, no drama.

Where RAG quietly falls short

Real policy work isn’t linear. It’s a tangle of:

  • changing interpretations
  • contradictory drafts
  • multi-year consultations
  • inter-department dependencies
  • supporting data and evaluation cycles

A top-N retrieval system can only surface the closest chunks — not the most important ones. Relevance isn’t the same as significance. The key insight you need might live in the eighth most relevant document, or the seventeenth, but RAG won’t fetch those unless you ask very precisely.

Chunking documents makes things worse. Splitting long reports into slices means the relationships across the whole document vanish. The LLM then tries to fill those gaps with pattern-guessing, which sometimes works and sometimes produces confident nonsense. Either way, you don’t want to take that to a Director-General.

Why full analytical pipelines matter

Policy teams don’t just read documents; they integrate evidence. That evidence often spans:

  • evaluation datasets
  • operational performance metrics
  • demographic or environmental data
  • stakeholder submissions
  • multi-year reporting cycles

Answering questions like:

  • “How have outcomes changed?”
  • “What interventions are showing effect?”
  • “Where are the gaps or risks?”

…requires computation, not retrieval.

Analytics engines provide trend detection, modelling, aggregation, provenance, and repeatability. They do the slow, disciplined work that good policy depends on. RAG doesn’t replace that. It never will.

Why over-relying on RAG is risky

The danger isn’t that RAG is bad. It’s that it’s convincing.

A RAG chatbot will happily produce a fluent summary even if:

  • it only saw part of the picture
  • it retrieved outdated or draft material
  • the real answer spans ten documents instead of three
  • there’s contradictory evidence it never saw

You get speed, but you trade depth. Sometimes that’s fine. Sometimes that’s fatal to good policymaking.

RAG vs Analytics vs Hybrid — and where GraphRAG fits

A realistic tech stack for policy departments includes all three.

Use RAG for:

  • lookups
  • summarisation
  • locating key paragraphs
  • chat-style internal support

Use analytics for:

  • evidence-based advice
  • trend analysis
  • impact evaluation
  • defensible decision support

Use a hybrid when:

  • natural language is desired
  • you need to blend documents with data
  • you want deeper synthesis without full querying overhead

And then there’s the missing middle: GraphRAG

GraphRAG flips the model. Instead of retrieving chunks, it builds a knowledge graph across your documents — capturing:

  • relationships
  • dependencies
  • definitions
  • contradictions
  • themes
  • causal links

This turns your document library into something closer to a structured conceptual map. It enables multi-hop reasoning: the kind of cross-document linkage that policy work demands.

RAG is a librarian.
Analytics is an evidence lab.
GraphRAG is the index of relationships you wish your SharePoint library had in 2013.

A practical architecture for policy teams

A robust setup looks like this:

Layer 1 — RAG:

Fast lookups from curated documents.

Layer 2 — GraphRAG:

Context retrieval based on relationships, not just similarity.

Layer 3 — Analytics Engine:

Evidence, modelling, trends, metrics, KPIs, impacts, performance.

Layer 4 — Orchestration / Agents:

Route each question to the right subsystem. Blend outputs. Provide reasoning traces.

This mirrors how policy people actually think. Quick answers for small things. Deep answers where it counts.

What’s next in the RAG world

The ecosystem is evolving quickly:

  • GraphRAG (Microsoft) — knowledge-graph-driven retrieval
    github.com/microsoft/graphrag
  • Long-context models — reducing the need for chunking
  • LongRAG — retrieving large coherent sections, not fragments
    arxiv.org/abs/2406.15319
  • Adaptive retrieval systems — adjusting how many documents to fetch
  • Multi-agent pipelines — separate agents for retrieval, analysis, critique, and synthesis

These approaches are more capable of handling the messy, relational nature of policy content.

Why this matters

Top-N RAG chatbots are fantastic — but they’re a screwdriver. Policy analysis is more of a workshop.

If you want:

  • speed → use RAG
  • depth → use analytics
  • relationships → use GraphRAG
  • all of the above → orchestrate them

Real-world policy work demands fast access to documents and deep evidence and structured reasoning. No single model gives you all of that, but a layered system does.

Further Reading

Written for KiwiGPT.co.nz — Generated, Published and Tinkered with AI by a Kiwi