How to Optimise your RAG — Enhancing LLM Accuracy with a Dictionary-Based Approach (Part 2/3) | by MD. SHARIF ALAM | Mar, 2025

Massive Language Fashions (LLMs) are revolutionizing AI-driven knowledge interactions, however they typically battle with hallucinations, indecisiveness, and inaccurate knowledge interpretation — challenges that may be vital in enterprise purposes.

To handle these points, we introduce a Dictionary-Primarily based Programmatic and Algorithmic Method — a system that acts as an clever middleman between the LLM and structured knowledge. This strategy ensures precision, reliability, and domain-specific accuracy whereas optimizing question execution.

The Dictionary System enhances LLM efficiency in 5 key methods:

1. Area-Particular Responses — Ensures accuracy by tailoring queries to industry-specific datasets.
2. Prevents Hallucinations — Validates database schemas earlier than question execution.
3. Filters Irrelevant Queries — Stops incorrect or out-of-context questions from producing false outcomes.
4. Suggestions Optimization — Repeatedly learns from person corrections and question historical past.
5. Environment friendly Question Execution — Reduces token prices and enhances velocity by fetching solely related schema fragments.

The Dictionary System integrates a number of parts to refine LLM-driven queries earlier than execution:

1. Good Schema Dealing with

As an alternative of loading a complete database schema, the Dictionary fetches solely related desk and column fragments, enhancing effectivity.

Instance:

If a database has 200 tables however a question issues solely Orders and Clients, the Dictionary fetches solely these schemas, lowering computation time.

Instance:

Optimized Schema Retrieval

With out Dictionary Method:

Database Dimension: 200 tables

Question Wants: Solely 2 tables (Orders, Clients)

Difficulty: LLM masses total schema → Gradual & Inefficient

With Dictionary Method:

Database Dimension: 200 tables

Question Wants: Orders & Clients

Optimization: Fetches solely these two tables → 90% discount in schema measurement!

Earlier than executing a question, the system verifies if the requested desk, column, or relationship exists — stopping invalid queries.

Instance:

Person Question: “Discover complete gross sales grouped by class.”
Difficulty: No class column exists, however product_type does.
Answer: The Dictionary auto-corrects and generates:

SELECT product_type, SUM(gross sales) FROM Orders GROUP BY product_type;

Instance:

Schema Validation in Motion

With out Schema Validator:

Person Question: “Discover complete gross sales grouped by class.”
Error: "Column 'class' doesn't exist."

With Schema Validator:

System Detects: "class" doesn’t exist, however "product_type" does.
Auto-Corrected Question:

SELECT product_type, SUM(gross sales) FROM Orders GROUP BY product_type;

Question Executes Efficiently!

Advantages:

Prevents Invalid Queries — No extra errors as a result of incorrect desk/column names.
Improves Accuracy — Suggests right phrases when potential.
Enhances Effectivity — Saves time by avoiding pointless debugging.

By monitoring previous question successes and failures, the system refines responses and prevents repeated errors.

Question Efficiency Over Time

The system constantly improves by analyzing frequent person modifications to AI-generated queries.

Course of:

1. Monitor modifications customers make to AI-generated queries
2. Determine patterns in these modifications
3. Routinely incorporate widespread changes into future question era
4. Repeatedly refine based mostly on ongoing person interactions

A domain-specific data base shops continuously used queries, schema insights, and entry management insurance policies.

Key Parts:

1. Most Used Queries
2. Database Schema Info
3. Safety Insurance policies & Information Entry Controls
4. Question Optimization Methods

The Dictionary Method is only the start!
Subsequent Steps:

• Automated High quality-Tuning: Utilizing reinforcement studying to enhance question accuracy.
• Dynamic Schema Updates: Adapting to real-time database modifications for seamless execution.

As AI-driven knowledge interactions increase, programmatic options just like the Dictionary System will probably be important for making LLMs extra correct, dependable, and cost-effective in structured knowledge environments.

[1] Brown, T., et al. (2023). “Mitigating hallucinations in giant language fashions via structured validation.” Journal of Synthetic Intelligence Analysis, 68(3), 1245–1287.

[2] Johnson, L., & Patel, S. (2024). “Information graph integration for enhanced LLM accuracy in domain-specific purposes.” Proceedings of the Worldwide Convention on Information Discovery and Information Mining, 412–428.

[3] Zhang, Y., et al. (2023). “Schema-aware question processing for big language fashions.” Transactions on Database Techniques, 15(2), 178–196.

[4] Kumar, R., & Chen, M. (2024). “Reinforcement studying approaches to LLM question optimization.” IEEE Convention on Machine Studying Purposes, 327–339.

[5] Roberts, A., et al. (2023). “Enhancing factuality in language fashions via retrieval augmentation and schema validation.” Transactions on Machine Studying, 15(3), 278–296.

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