Grounding AI in What You Actually Know – Sysero
Law firms have spent years building proprietary knowledge libraries: approved precedents, practice notes, model clauses, checklists, and internal policies. The promise of AI is that it can make this knowledge instantly accessible. The reality is more complicated — but three approaches are turning that promise into something practical.
1. RAG: making AI read your library, not guess
Retrieval Augmented Generation works by searching a firm’s own knowledge base first, retrieving the most relevant documents, and passing that content as context to the AI. When a lawyer asks “What is a building liability order?” the AI isn’t improvising from its training data — it’s finding the firm’s own practice notes and synthesising an answer from approved content. The source documents are cited, so the lawyer can verify the answer in seconds.
The hallucination problem doesn’t vanish, but it shrinks dramatically when the AI reads from a library you trust rather than generating from memory. The prerequisite is structured knowledge management: documents categorised by practice area, resource type, and jurisdiction, maintained through proper review workflows. RAG is only as good as the knowledge it retrieves.
2. The questions RAG struggles with
RAG handles many queries well, but analysis of real prompts reveals categories where traditional vector search falls short.
Document discovery queries like “Show all employment precedents” or “Find all IP bulletins referring to SkyKick” require the system to filter by practice group and resource type simultaneously — something semantic similarity alone cannot do. Searching for every document mentioning a client name like “Thatchers” needs
entity extraction and linking across the library, not just keyword matching.
Procedural queries are harder still. “List every document required for a Gateway 2 submission” means synthesising information across practice notes, checklists, and legislation documents — understanding that Gateway 2 is part of the HRB Gateway Regime, which relates to the Building Safety Act 2022. Vector search
retrieves fragments; what’s needed is connected knowledge.
Perhaps most overlooked are the needs of KM professionals. They don’t use the system like fee-earners. They need to challenge the knowledge base: which practice areas lack up-to-date practice notes? Which precedents reference superseded legislation? Which topics are missing checklists? These are reporting and accountability questions that require structured metadata — not just what documents say, but when they were last updated, who authored them, and how they relate to each other. Generic AI has no concept of document freshness or coverage gaps.
Serving both lawyers and KM teams requires going beyond simple vector retrieval to combine semantic search with structured metadata filtering, entity resolution, and relationship traversal. Better-maintained knowledge leads to better AI responses, which drives adoption, which helps KM teams see where investment is needed.
3. AI entity extraction meets matter data
The third frontier moves from retrieval to document creation. A lawyer selects an approved template from the knowledge library and pastes in matter-specific content — an instruction email or transaction details. AI extracts the entities: client names, dates, monetary values, key terms. These are mapped to placeholders in the template, producing a matter-ready document in seconds.
The difference from generic AI drafting is fidelity and control. The output preserves the original template’s formatting — headers, footers, styles, clause structure — because the AI is populating an approved document, not inventing one. The lawyer reviews extracted entities before the document is produced.
For routine documents, this can be fully automated. For complex, high-value work, the approach extends: subject matter experts define custom fields, optional clauses, and repeating data sections within templates, and AI extraction pre-populates what it can while flagging the rest for human input. Every document starts from approved, managed content.
The economics shift. Instead of hours spent manually populating templates, the first draft arrives in minutes — freeing time for the substantive legal judgment clients are actually paying for.
The principle
These three capabilities share a single idea: AI is most useful when grounded in knowledge you’ve already vetted. The firms that benefit most aren’t chasing the latest model — they’re investing in the quality and structure of their knowledge libraries, because that determines whether AI delivers reliable answers or confident-sounding nonsense.
