Your LLM just drafted a services agreement. It looks polished. It reads like a contract. Then your lawyer flags it: the payment dispute clause cites a jurisdiction you don't work in, the termination language contradicts your standard, and three indemnification sections don't exist in any contract your firm has ever used. The AI didn't make typos. It hallucinated clauses. This is the contract drafting paradox. Language models cut first-draft time by 60% or more. They also invent terms with the confidence of someone who has read every contract ever written—which they have, and which is exactly the problem. An LLM trained on thousands of agreements doesn't know which ones you want to use. Three guardrails stop hallucinations cold: locking structure to templates, grounding AI in your own approved clause library, and forcing a human checkpoint before any signature. Each works alone; together they let AI handle the mechanical work while your lawyer focuses on the actual risk. Guardrail 1: Template-only mode locks structure, varies only terms The simplest and most effective guardrail is the most boring: do not let the LLM write the contract. Let it fill in the contract. Template-only mode means you build the skeleton once—headings, clause order, section numbers—and the AI only varies the details that change between deals: prices, dates, party names, and specific scope language. The clausework stays locked. Here's what this looks like in practice. You have a master services agreement (MSA) with twelve sections: definitions, scope, term, payment, ip ownership, confidentiality, limitation of liability, indemnification, termination, governing law, dispute resolution, and miscellaneous. Every MSA your firm signs uses these twelve sections in this order. A guardrailed system doesn't ask the LLM to write an MSA. It asks the LLM to: Fill in the client name, address, and authorized signatories in the preamble. Rewrite the scope section based on the statement of work you paste in. Calculate the payment schedule based on the rate card and term you specify. Copy the remaining nine sections verbatim from the template. The LLM never touches indemnification, limitation of liability, or governing law. It cannot invent a new dispute resolution process. It has no authority to create a clause. Tools like PandaDoc and Ironclad enforce this pattern through their template builders. You define conditional blocks (e.g., "show confidentiality termination clause only if contract term exceeds 3 years"), but the clauses themselves are hard-coded. The AI fills in calculated fields and variable text, not structure. Template-only mode is not creative. It is not flexible. It is exactly the point. Creativity is where hallucination lives. Guardrail 2: Vector knowledge base of approved clauses grounds AI in your playbook Template-only works if your contracts are simple and repetitive. But most aren't. Payment terms vary by client size. Liability caps depend on deal value. You sometimes agree to specific performance clauses; sometimes you don't. You need flexibility without opening the door to fabrication. This is where a knowledge base guardrail earns its place. You build a vector database—a searchable library of every clause you have ever approved. Not every clause in existence. Yours. The workflow looks like this: You upload or paste your approved clauses into a structured library. Each clause is tagged by type (payment, liability, IP, termination, etc.), applicable deal size, and jurisdiction. If you have five versions of a payment clause for different client tiers, all five go into the knowledge base, tagged accordingly. When the LLM needs to draft or modify a clause, it first searches the knowledge base. "Find payment clauses for deals under $50K." The vector search returns the three versions you have actually used. The LLM is instructed to draft only by modifying or selecting from the results. It cannot invent new language; it can only adapt what it finds in your library. The difference is profound. Without a knowledge base, the LLM has no anchor. It defaults to generic language it has seen millions of times. With a knowledge base, the LLM learns your firm's voice, your preferred structure, and your risk thresholds. The output reads like your work because it literally is your work. This approach requires a custom integration or a tool that supports vector search on custom documents. Ironclad and specialized contract AI platforms like LawGeex support this. You can also build it yourself using a vector database like Pinecone or Weaviate and OpenAI's embedding API—a weekend project for a developer. The payoff is not just hallucination prevention. Your contracts become more consistent. Your legal review gets faster because the drafts already match your firm's playbook. And when you do need to negotiate a clause, the LLM suggests variants that you have already approved, not variants it guessed. Guardrail 3: Human checkpoint before signature closes