Bounding the Semantic Horizon of Fine-Tuned LLMs: Controlling Knowledge Bases

Introduction

As large language models (LLMs) become increasingly prevalent in enterprise applications, ensuring that they generate responses based on trusted and relevant sources is a major challenge. Despite their impressive capabilities, LLMs are inherently open-ended, meaning they attempt to answer questions even when they lack the necessary domain knowledge. This can lead to hallucinations, misinformation, or non-compliance with defined knowledge boundaries.

For businesses and technical teams leveraging fine-tuned LLMs, bounding the semantic horizon—or controlling how and from where a model retrieves information—is a critical concern. Whether in customer support, financial advisory, or corporate AI assistants, ensuring that an LLM only responds using predefined knowledge sources significantly enhances accuracy, compliance, and security.

This blog explores key techniques to constrain LLM knowledge bases, covering methods such as data filtering, prompt engineering, and retrieval-augmented generation (RAG). Additionally, we discuss how semantic vs. keyword-based retrieval influences embeddings and how advanced knowledge base configurations enable models to answer exclusively from curated datasets.

The Challenge of Controlling LLM Knowledge

LLMs, by design, are trained on massive amounts of publicly available data. However, many enterprise applications require highly specific and proprietary knowledge, which may not be covered in standard training datasets. Fine-tuning a model on proprietary data improves domain-specific accuracy, but it does not necessarily prevent the model from extrapolating beyond its intended scope. Even with strict fine-tuning, a model might still attempt to answer questions outside its dataset based on general language patterns.

For example, a chatbot trained only on a company’s policies should ideally respond exclusively based on those policies. However, if prompted, it might still generate plausible-sounding yet incorrect responses using its general linguistic knowledge. Similarly, a financial advisory assistant should provide answers based solely on approved investment guidelines, not personal opinions derived from unrelated datasets.

This challenge leads to a fundamental question: How do we ensure LLMs provide responses only from an authorized and controlled knowledge base? The answer lies in multi-layered techniques, combining fine-tuning, retrieval-augmented generation (RAG), prompt engineering, and entity recognition to filter, constrain, and validate responses.

Key Techniques for Bounding an LLM’s Knowledge

1. Data Filtering and Fine-Tuning

Fine-tuning an LLM on a narrow, curated dataset helps restrict its knowledge domain. This process involves training the model exclusively on verified content, such as corporate policies, internal documentation, and approved FAQs. However, fine-tuning alone is not a foolproof solution, as models retain a vast amount of prior general knowledge.

To further refine responses, data filtering mechanisms can be used to preprocess training datasets, removing irrelevant or overly broad information. However, even with strict filtering, fine-tuned models may still attempt to "guess" answers when they lack direct knowledge.

2. Prompt Engineering for Controlled Responses

Prompt engineering helps shape model behavior by instructing it explicitly on how to handle out-of-scope queries. Effective techniques include:

Contextual Constraints: Directing the model to only use specified information sources.
Strict Response Formatting: Defining response structures that enforce compliance (e.g., “If unsure, respond with 'I do not have this information.'”).
Example-Based Conditioning: Providing multiple examples within the prompt to reinforce the desired response format.

However, prompt engineering alone cannot guarantee absolute compliance—especially when users find ways to circumvent guardrails. Hence, additional retrieval-based mechanisms are required.

3. Retrieval-Augmented Generation (RAG) for Dynamic Knowledge Control

One of the most effective ways to bound LLM knowledge is retrieval-augmented generation (RAG). This approach ensures that model responses are based only on retrieved external documents rather than pre-trained knowledge alone.

RAG operates through:

Query Understanding & Rewriting: The system reinterprets user queries into well-formed search queries.
Semantic Retrieval: Queries are matched against a vector database containing curated knowledge sources.
Knowledge Injection: Retrieved documents are inserted into the prompt before the LLM generates a response.
Filtered Response Generation: The LLM produces answers based solely on the retrieved context.

4. Semantic vs. Keyword-Based Retrieval for Embedding Control

Embedding models dictate how retrieved documents are ranked and matched to user queries. A robust RAG system incorporates two adjustable modes:

Semantic Search: Uses deep embeddings to match concepts, even when keywords differ.
Keyword-Based Search: Matches exact terms for precision.

By adjusting these parameters, administrators can fine-tune how retrieval prioritizes results, balancing flexibility (semantic) and strict control (keyword-based retrieval).

Advanced Knowledge Base Configurations for Maximum Control

To fully constrain an LLM’s knowledge to predefined sources, additional control mechanisms can be employed:

1. Pre-Processing Input Filters

Incoming queries are analyzed before reaching the model.
Non-relevant queries (e.g., “What is the weather today?”) are blocked before processing, preventing out-of-scope responses.

2. Post-Processing Response Validation

Every LLM-generated response is scanned for compliance.
If an answer lacks a verifiable source from the knowledge base, it is discarded or reprocessed.

3. Continuous Monitoring and Improvement

LLM Training Logs track cases where the model attempts to respond without enough data.
New documents and FAQs are dynamically added to expand knowledge without re-training.

Enforcing Knowledge Boundaries in Fine-Tuned LLMs

Controlling an LLM’s knowledge base is not a single-step solution but rather a multi-layered strategy. Techniques like fine-tuning, prompt engineering, retrieval-augmented generation (RAG), entity recognition, and embedding control must work together to ensure that AI-generated responses remain accurate, compliant, and relevant.

By applying these techniques, organizations can maintain highly accurate, role-specific AI assistants, reducing hallucinations and improving user trust.

As AI adoption grows, bounding the semantic horizon of fine-tuned LLMs will be essential for maintaining control, security, and compliance in enterprise AI applications.