Source-constrained prompting

About

Source-Constrained Prompting is a technique where the model is explicitly instructed to use only the provided information (context/source) and avoid using its own general knowledge.

Unlike basic context injection, this technique adds a strict constraint:

The model must not go beyond the given source.

Core idea:

Limit the model’s knowledge scope to prevent hallucination.

This is typically used along with:

• Retrieved documents (RAG)

• API specifications

• Policies and rules

• Internal documentation

Why Source Constraint Is Critical ?

Even when context is provided, LLMs tend to:

• Mix provided data with pre-trained knowledge

• Fill missing gaps with assumptions

• Add “helpful” but incorrect details

This leads to:

• Hallucinated facts

• Inconsistent outputs

• Non-auditable responses

Source constraint ensures:

• Responses are grounded

• No external assumptions are introduced

• Outputs are traceable to given data

This is critical in:

• Compliance systems

• Financial workflows

• Legal or policy-based reasoning

• Internal enterprise tools

The Purpose of Source-Constrained Prompting

This technique aims to:

1. Eliminate hallucination

2. Ensure strict adherence to provided data

3. Improve auditability and traceability

4. Enforce domain boundaries

5. Increase reliability in high-risk systems

It transforms the model from:

Creative generator → Controlled reasoning engine

Where it Fits in the Prompt Lifecycle

User Query
      ↓
Retrieval / Context Injection
      ↓
Source Constraint (Restrict to provided data)
      ↓
Reasoning
      ↓
Output Generation

It acts as a control layer over knowledge usage.

Different Source Constraint Patterns

1. Strict Source Usage

Instruction:

“Answer using only the provided context.”

Basic but essential.

2. No-Assumption Constraint

Instruction:

“If information is not available in the context, return ‘Not Available’.”

Prevents guessing.

3. Evidence-Based Answering

Instruction:

“Support each answer with reference to the provided content.”

Forces grounding.

4. Context-Only Reasoning

Instruction:

“Do not use prior knowledge. Rely strictly on the given data.”

Stronger enforcement for sensitive systems.

Common Mistakes

1. Weak Constraint Language

Weak: “Use the context if helpful.”

Strong: “Use only the provided context. Do not add external information.”

Clarity matters.

2. Missing Fallback Behavior

If the model cannot find an answer:

• It may hallucinate

Always define:

“What to do if data is missing.”

3. Poor Context Quality

Even with strict constraints:

• If context is incomplete → output will be incomplete

Garbage in → garbage out.

4. No Structure in Context

Unstructured context:

• Makes it hard for model to locate relevant information

• Reduces effectiveness of constraint

Combine with structured input formatting.

Sample Prompts

Without Source Constraint

Explain the refund policy based on the following document.

<policy text>

Issue:

• Model may add assumptions

• May generalize beyond document

With Source-Constrained Prompting

Use only the provided policy document to answer the question.

If the answer is not present, return "Not Available".

Context:
---
<policy text>
---

Question:
Explain the refund policy.

Benefits:

• No hallucination

• Traceable output

• Reliable response