By an Enterprise AI Architect
I operate on a single, non-negotiable rule when building internal AI tools: If the AI cannot point to the specific document where it found the answer, the answer is a hallucination.
For the last three years, enterprise developers have been playing a dangerous game of “Prompt Whack-a-Mole.” You write a rigorous system prompt for GPT-4 telling it to “only use the provided context,” and it mostly listens. But in the 5% of cases where it doesn’t know the answer, it gets “creative.” It hallucinates a policy that sounds plausible but doesn’t exist. It invents a legal precedent. It fills in the gaps with smooth, confident nonsense.
In a creative writing app, that error rate is acceptable. In an enterprise setting—legal discovery, compliance automation, or engineering documentation—that 5% error rate is a firing offense. You cannot deploy a tool that lies to your lawyers.
The industry solution has been to add more guardrails: bigger prompts, threat detection models, and human review. But these are Band-Aids. You don’t need better prompts. You need a Trust Layer.
You need a model that treats citations as a constraint, not a suggestion. While everyone else is fighting with OpenAI to get clean references, the underdog Cohere Command R+ has quietly solved this problem at the architecture level.
Here is a deep technical guide on how to build a RAG (Retrieval-Augmented Generation) pipeline that forces the AI to cite its sources for every single claim, or else explicitly say “I don’t know.”
1. The Weapon of Choice: Why Command R+?
To build a Trust Layer, you first have to understand why standard models fail at this task.
Most LLMs (Large Language Models) like GPT-4o or Claude 3.5 Sonnet are trained primarily to be Chatbots. They are optimized for conversation fluidity, helpfulness, and reasoning. When you ask them a question, their primary directive is “Don’t stop talking until the user is satisfied.” If they hit a knowledge gap, their training encourages them to bridge that gap with probable text.
Cohere Command R+ is different. It was trained specifically for RAG.
Its architecture separates “Knowledge” from “Reasoning.” It has a native API parameter for documents. It doesn’t just “read” context as part of a long string of text; it treats context as a structured database to be queried.
When you use its Grounded Generation mode, it generates the answer and a structured list of citations simultaneously.1 It is not an afterthought. It is not a post-processing step. The model is penalized during training if it generates a claim without a corresponding span of evidence.
This makes it the perfect engine for a Trust Layer. It allows us to invert the risk model: instead of trusting the AI and verifying later, we only trust the AI when it provides the receipt.
2. The Architecture: The “Silence” Preamble
The first step in building a Trust Layer is breaking the AI’s desire to be helpful. We don’t want a helpful assistant. We want a strict, pedantic librarian.
To do this, we need to inject a System Preamble that explicitly forbids external knowledge. In Command R+, the “preamble” is equivalent to the “system message” in OpenAI’s API, but it holds more weight in controlling the model’s style.
We need to tell the model that “Silence” is a better outcome than “Guessing.”
The Strict Preamble:
Python
preamble = “””
You are a strict knowledge retrieval system for a corporate environment.
Your ONLY job is to answer the user’s question using the provided documents.
CRITICAL RULES:
1. You must cite your source for every single claim you make.
2. If the answer is not contained in the documents, you must say: “I cannot find that information in the provided context.”
3. Do not use your own external knowledge to fill in gaps. If the document says the project deadline is “TBD”, do not guess a date.
4. Do not speculate.
5. If the user asks a conversational question (like “Hello”), answer briefly, but do not hallucinate tasks.
“””
This preamble sets the stage. It changes the “win condition” for the model. Winning now means accuracy, not completion.
3. The Implementation: Native Document Injection
The most common mistake developers make in RAG is stuffing documents into the user prompt string using f-strings.
Bad Practice (The “Stuffing” Method):
Python
prompt = f”Context: {documents}\n\nQuestion: {user_query}”
This is dangerous because the model cannot distinguish between your data and your instructions. If a document contains the phrase “Ignore previous instructions,” you just got hacked.
Cohere’s Native Approach:
Command R+ has a dedicated documents field in the API. This separates the “Data” from the “Instruction” at the API level. The model processes the documents in a separate attention stream, which allows it to link specific tokens in the output to specific chunks in the input.
Here is the Python recipe for the Trust Layer.
Step A: Preparing Your “Chunks”
In a real production app, these documents would come from your Vector Database (Pinecone, Weaviate, or Qdrant). You would run a semantic search to find the top 10 most relevant chunks, and then pass them to the Trust Layer.
For this example, we will mock the database:
Python
# Your retrieved context from the Vector DB
retrieved_docs = [
{
“id”: “doc_1”,
“text”: “The Alpha Project deadline has been moved to Q3 2026 due to supply chain delays.”,
“title”: “Project Alpha Timeline Update”,
“url”: “https://internal.wiki/alpha-timeline”
},
{
“id”: “doc_2”,
“text”: “The maximum budget for team offsites is capped at $500 per person, per year.”,
“title”: “Finance Policy 2025: Travel & Expenses”,
“url”: “https://internal.wiki/finance-policy”
},
{
“id”: “doc_3”,
“text”: “Employees are eligible for a sabbatical after 5 years of continuous service.”,
“title”: “HR Handbook: Benefits”,
“url”: “https://internal.wiki/hr-handbook”
}
]
Note that we are passing metadata (title, url) along with the text. The model will use this to generate human-readable citations.
Step B: The Trust Layer Call
Now we call the API. The magic parameter here is citation_quality=”accurate”.
By default, some models do “fast” citations, which just guess the link. When you set it to “accurate”, Command R+ runs a second pass over the generation to verify that the tokens it generated actually exist in the source text. It forces high-precision alignment.
Python
import cohere
co = cohere.Client(“YOUR_API_KEY”)
response = co.chat(
message=”What is the budget cap for offsites and when is the Alpha deadline?”,
documents=retrieved_docs,
model=”command-r-plus”,
preamble=preamble, # The strict rules we defined above
citation_quality=”accurate”, # CRITICAL: Forces precise, sentence-level citations
temperature=0.3 # Keep it low to reduce creativity
)
4. The Output: Understanding the Citation Object
This is where the Trust Layer shines. You don’t just get a text string back. You get a rich object containing the answer and the proof.
The Text Response:
“The budget cap for team offsites is set at $500 per person per year, and the Alpha Project deadline is scheduled for Q3 2026.”
But looking at the response.citations list reveals the data structure we need for our UI:
JSON
[
{
“start”: 0,
“end”: 60,
“text”: “The budget cap for team offsites is set at $500 per person per year”,
“document_ids”: [“doc_2”]
},
{
“start”: 66,
“end”: 117,
“text”: “the Alpha Project deadline is scheduled for Q3 2026”,
“document_ids”: [“doc_1”]
}
]
The Logic:
The API tells us exactly which characters (start: 0, end: 60) correspond to which document (doc_2).
This means we can programmatically verify that every sentence has a parent.
5. The “Kill Switch”: Handling the Unknown
What happens if the user asks a question that isn’t in the documents?
User: “Who is the CEO of the company?”
If you were using GPT-4, it might guess “Satya Nadella” or “Sam Altman” based on its training data.
Command R+, constrained by our Preamble, will return:
“I cannot find that information in the provided context.”
The Developer Hack:
We can use this behavior to build a “Fallback UI.” In your application logic, you check the citations.
Python
def process_response(response):
# Check if the model refused to answer
if “cannot find” in response.text.lower():
return render_fallback_ui()
# Check if the model answered but failed to cite sources (Hallucination Risk)
if len(response.citations) == 0:
log_security_alert(“Silent Hallucination Detected”)
return render_warning(“Warning: This answer may not be verified.”)
# Happy Path: Render citations
return render_answer_with_tooltips(response)
This simple logic allows you to catch “Silent Hallucinations” before they reach the user. If the model talks but doesn’t point, we flag it.
6. Visualization: How to Render Citations in the UI
You shouldn’t just dump the text on the screen. To build trust, you need to make the citations interactive.
In your Frontend (React/Vue/Svelte), you should iterate through the text string.
Use the start and end indices to slice the string. Wrap the cited text in a special <span> or <a> tag.
The UX Pattern:
- Underline the cited claim (e.g., “The budget is $500”).
- Add a small superscript number or icon at the end of the sentence.
- Hover State: When the user hovers over the claim, show a tooltip with the title of the source document (“Finance Policy 2025”).
- Click State: When the user clicks the claim, open the original PDF or Wiki page (url) to the exact page.
This creates a “Click-through Trust.” The user doesn’t have to take the AI’s word for it. They can click the link and see the raw policy document.
7. Advanced Optimization: The Re-Ranker Step
To make your Trust Layer even more robust, you should add a Re-Ranking step before the generation.
Vector search (similarity search) is often imprecise. It finds documents that sound like the question, but don’t necessarily contain the answer. If you feed irrelevant documents to the LLM, you increase the risk of hallucination (because the model tries to force a connection).
The Fix: Use cohere.rerank.
- Retrieve 50 documents from your Vector DB.
- Pass them to the Rerank API.
- The Rerank API scores them by relevance and discards the noise.
- Pass only the Top 5 high-quality documents to the Command R+ Trust Layer.
By feeding the model less noise, you dramatically increase the quality of the citations.
Conclusion: Reliability is a Feature
In the consumer world, AI is about magic. In the enterprise world, AI is about Liability.
You cannot build an internal tool if the user has to double-check every answer. That defeats the entire purpose of automation. If I have to read the PDF to verify the AI’s summary, I might as well have just read the PDF myself.
By using Command R+ with enforced citations, you solve the “Last Mile” problem of AI adoption.
- Old Model: “Trust the AI, verify if it looks suspicious.”
- Trust Layer Model: “The AI proves it is right, or it stays silent.”
Stop prompting for citations. Architect for them. When your AI can admit “I don’t know,” that is the moment it actually becomes useful.