PROVISIONAL PATENT APPLICATION

Filing Date: February 8, 2026

Title of Invention:
SYSTEM AND METHOD FOR MODULAR CONTEXT ASSEMBLY AND QUALITY-OPTIMIZED PROMPT GENERATION FOR LARGE LANGUAGE MODELS

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority and is entitled to the filing date pursuant to 35 U.S.C. §119(e).

FIELD OF THE INVENTION

This invention relates generally to artificial intelligence systems, and more specifically to systems and methods for structuring, assembling, validating, and optimizing contextual information (prompts) provided to Large Language Models (LLMs) to improve response quality, consistency, and reliability.

BACKGROUND OF THE INVENTION

Current State of the Art

Large Language Models (LLMs) such as GPT, Claude, Gemini, and similar systems have revolutionized natural language processing. However, their effectiveness heavily depends on the quality and structure of the input prompts (contextual information) they receive.

Problems with Existing Approaches

Current prompt engineering methods suffer from several critical limitations:

1. Unstructured Prompts: Most implementations concatenate text strings without validation or structural organization, leading to inconsistent results.
2. No Quality Metrics: There is no systematic way to evaluate prompt quality before execution, resulting in trial-and-error approaches.
3. Provider Lock-in: Implementations are typically tied to specific LLM providers, requiring complete rewrites when switching providers.
4. Lack of Reusability: Effective prompt patterns cannot be easily packaged, shared, or reused across different use cases.
5. No Cognitive Structure: Prompts do not leverage established reasoning patterns (comparative analysis, causal reasoning, step-by-step decomposition, etc.) in a systematic way.
6. Manual Context Management: Multi-turn conversations require manual tracking and assembly of conversation history.

Need for the Invention

There is a need for a system that provides: structured, modular context assembly with validation; automatic quality evaluation of prompts before execution; provider-agnostic abstraction layer; reusable cognitive templates for common reasoning patterns; and intelligent context transformation and optimization.

SUMMARY OF THE INVENTION

The present invention provides a novel system and method for assembling, validating, optimizing, and executing contextual information for Large Language Models through a modular, quality-aware architecture.

Key Innovations:

• Modular Context Architecture: Directive, Guidance, Constraints, and Sources components.
• Quality Metrics Engine: Automated evaluation across clarity, specificity, actionability, structure, and completeness.
• Cognitive Template System: Pre-engineered templates for comparative analysis, causal reasoning, step-by-step decomposition, and 40+ additional patterns.
• Provider Abstraction Layer: Unified execution across multiple LLM providers.
• Intelligent Context Transformation: Conversion between LangChain, LlamaIndex, and native formats.

DETAILED DESCRIPTION OF THE INVENTION

System Architecture

The invention comprises a Context class with directive (required), guidance, constraints, and sources; methods assemble(), execute(), validate(), and quality_score(); a Quality Metrics Engine evaluating clarity, specificity, actionability, structure, and completeness with weighted overall score; a Template system with base class and 50+ specialized templates (Analysis, Decision, Creative, Reasoning, Communication, Planning, Problem-Solving); a Provider Abstraction Layer routing to OpenAI, Anthropic, Google, Azure, and local providers with normalized response; Session Management for multi-turn context with history summarization; and a Transformation Engine for to_langchain(), to_llamaindex(), and serialization.

CLAIMS

Claim 1: A computer-implemented method for assembling and optimizing contextual information for large language models, comprising: (a) receiving modular components comprising at least a directive; (b) validating structural integrity; (c) evaluating quality across multiple dimensions; (d) assembling into an optimized prompt; (e) executing against a selected provider; (f) returning a normalized response.

Claim 2: A system comprising: modular component architecture; quality metrics engine; provider abstraction layer; template system; transformation engine.

Claims 3–10: Dependent claims as set forth in the full specification (quality evaluation details, guidance/constraints/sources, template system details, provider switching, session management, improvement suggestions, format conversion, 50+ templates).

ABSTRACT

A system and method for assembling, validating, and optimizing contextual information for Large Language Models through a modular architecture comprising directive, guidance, constraints, and sources. The invention includes a quality metrics engine, provider abstraction layer, and template system with 50+ cognitive patterns, enabling programmatic construction of high-quality prompts and provider-independent execution.

DRAWINGS

Figure 1: System architecture. Figure 2: Quality metrics flow. Figure 3: Template system. Figure 4: Provider abstraction. Figure 5: Session management. (Separate drawing files submitted.)

INVENTOR DECLARATION

I hereby declare that I am the original inventor of the subject matter which is claimed and for which a patent is sought. I hereby declare that all statements made herein of my own knowledge are true and that all statements made on information and belief are believed to be true.

Signature: _________________________   Date: _____________

Printed Name: Dhiraj Kumar Pokhrel

Entity Status: Micro Entity ☐   Small Entity ☐   Large Entity ☐

END OF PROVISIONAL PATENT APPLICATION — Specification