PROVISIONAL PATENT APPLICATION

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
• 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

• Directive: The core task or instruction
• Guidance: Role definition, behavioral rules, expertise areas, and communication style
• Constraints: Boundaries, requirements, format specifications, and quality criteria
• Sources: Reference materials, documents, data, and knowledge bases

• Clarity (lack of ambiguity, vague language)
• Specificity (concrete details, quantifiable metrics)
• Actionability (clear instructions, executable steps)
• Structure (logical organization, proper formatting)
• Completeness (coverage of requirements)

• Comparative Analysis
• Causal Reasoning
• Step-by-Step Decomposition
• Question Analysis
• Tradeoff Evaluation
• Risk Assessment
• Decision Frameworks
• And 40+ additional specialized templates

DETAILED DESCRIPTION OF THE INVENTION

System Architecture

The invention comprises several interconnected components:

1. Core Context Class

The Context class serves as the primary container for assembling modular components:

Context Object:

directive: Directive (required)
guidance: Guidance (optional)
constraints: Constraints (optional)
sources: List[Source] (optional)
metadata: Dict (system information)


Methods:

assemble() -> String: Compiles all components into optimized prompt
execute(provider, **kwargs) -> Response: Sends to LLM and returns result
validate() -> ValidationResult: Checks structural integrity
quality_score() -> QualityScore: Evaluates prompt effectiveness

2. Component Classes

Attributes:

content: String (the primary instruction)
context: String (optional background)
requirements: List[String] (specific needs)


Validation Rules:

Content must be non-empty
Must contain actionable verbs
Should specify expected output format

Attributes:

role: String (persona/perspective)
rules: List[String] (behavioral guidelines)
expertise: List[String] (knowledge domains)
style: String (communication approach)


Novel Aspect: Structured role definition with expertise mapping

Attributes:

boundaries: List[String] (what NOT to do)
format: OutputFormat (structured output specification)
quality: List[String] (quality criteria)
requirements: List[String] (must-have elements)

Attributes:

content: String (reference material)
metadata: Dict (title, author, date, type)
relevance: Float (0-1 score)


Methods:

chunk() -> List[Chunk]: Splits large sources
embed() -> Vector: Creates semantic embeddings

3. Quality Metrics Engine

QualityMetrics.evaluate(context) -> QualityScore:

Step 1: Clarity Analysis

Scan for vague words: "thing", "stuff", "maybe", "somehow"
Check for ambiguous pronouns without clear antecedents
Measure sentence complexity (Flesch-Kincaid)
Score: 0-1 (1 = perfectly clear)


Step 2: Specificity Analysis

Count concrete nouns vs abstract nouns
Identify quantifiable metrics and numbers
Check for specific examples vs generalizations
Score: 0-1 (1 = highly specific)


Step 3: Actionability Analysis

Identify action verbs (imperative mood)
Check for clear success criteria
Verify presence of deliverables/outputs
Score: 0-1 (1 = completely actionable)


Step 4: Structure Analysis

Evaluate logical flow and organization
Check formatting (bullets, numbering, sections)
Assess information hierarchy
Score: 0-1 (1 = well-structured)


Step 5: Completeness Analysis

Match requirements against content
Identify missing critical information
Check for necessary context
Score: 0-1 (1 = fully complete)


Step 6: Overall Score Calculation overall_score = weighted_average([ clarity * 0.25, specificity * 0.20, actionability * 0.25, structure * 0.15, completeness * 0.15 ])

Output: QualityScore object with:

overall: Float (0-1)
dimensions: Dict[Dimension, Float]
issues: List[Issue] (identified problems)
suggestions: List[String] (improvement recommendations)
strengths: List[String] (what's working well)

4. Template System

Template:

name: String
category: String (analysis, decision, reasoning, etc.)
parameters: Dict[String, Type] (required inputs)


Methods:

build_context(**params) -> Context: Constructs optimized context
execute(provider, **params) -> Response: Build and run
validate_params(**params) -> ValidationResult: Check inputs

ComparativeAnalyzer.build_context(
options: List[String], # Items to compare criteria: String, # Comparison dimensions context: String = None, # Background information depth: String = "detailed" # Analysis depth ) -> Context:

Generated Directive: "Conduct a comprehensive comparative analysis of the following options: [options formatted as numbered list]

Evaluate each option across these criteria: [criteria formatted as bullet points]

For each criterion: 1. Rate each option (0-10 scale) 2. Provide specific evidence 3. Note key tradeoffs

Conclude with:

Overall winner and why
Use case recommendations
Decision matrix summary"


Generated Guidance: Role: "Expert Analyst with deep experience in comparative evaluation" Rules:

Be objective and evidence-based
Acknowledge limitations in available data
Consider both quantitative and qualitative factors

Expertise: ["comparative analysis", "decision science", "evaluation frameworks"]

Generated Constraints:

Use consistent rating scales
Provide specific examples for each rating
Format output as structured comparison table
No unsupported claims

• Analysis patterns (SWOT, Gap, Trend, Anomaly, Data analysis)
• Decision patterns (Comparative, Tradeoff, Cost-benefit, Multi-objective)
• Reasoning patterns (Causal, Step-by-step, Analogical, Root cause)
• Creative patterns (Brainstorming, Idea generation, Design thinking)
• Communication patterns (Simplification, Persuasion, Audience adaptation)
• Planning patterns (Scenario, Resource allocation, Stakeholder mapping)
• Problem-solving patterns (Decomposition, Bottleneck identification, Constraint optimization)

5. Provider Abstraction Layer

Context.execute(provider: String, **kwargs) -> Response:

Step 1: Assemble Context prompt = self.assemble()

Step 2: Provider Routing if provider == "openai": client = OpenAI(api_key=kwargs.get('api_key')) response = client.chat.completions.create( model=kwargs.get('model', 'gpt-4'), messages=[{"role": "user", "content": prompt}], temperature=kwargs.get('temperature', 0.7), max_tokens=kwargs.get('max_tokens') )

elif provider == "anthropic": client = Anthropic(api_key=kwargs.get('api_key')) response = client.messages.create( model=kwargs.get('model', 'claude-3-opus'), messages=[{"role": "user", "content": prompt}], temperature=kwargs.get('temperature', 0.7), max_tokens=kwargs.get('max_tokens') )

elif provider == "google": # Google Gemini implementation

elif provider == "azure": # Azure OpenAI implementation

elif provider == "local": # Local model implementation (Ollama, LMStudio, etc.)

Step 3: Response Normalization return Response( content=extract_text(response), tokens_used=extract_tokens(response), model=model_used, provider=provider, raw_response=response, metadata=extract_metadata(response) )

6. Session Management

Session:

id: String (unique identifier)
messages: List[Message] (conversation history)
context: Context (base context for all turns)
metadata: Dict (tracking information)


Methods:

add_turn(user_input, assistant_response): Appends to history
get_context(max_tokens=None) -> Context: Returns assembled context with history
summarize() -> String: Compresses long histories
export() -> Dict: Saves session state
load(data: Dict) -> Session: Restores session


Algorithm for Context Assembly with History: 1. Start with base context (directive, guidance, constraints) 2. Add conversation history (newest first, up to max_tokens limit) 3. If history exceeds limit: a. Summarize older turns b. Keep recent turns verbatim 4. Return assembled context

7. Transformation Engine

Context.to_langchain() -> LangChainPrompt:
Converts Context object to LangChain PromptTemplate format

Context.to_llamaindex() -> LlamaIndexPrompt: Converts Context object to LlamaIndex prompt format

Context.to_dict() -> Dict: Serializes to JSON-compatible dictionary

Context.from_dict(data: Dict) -> Context: Deserializes from dictionary

Novel Aspect: Semantic-preserving transformations between frameworks

CLAIMS

Independent Claims

1. receiving a plurality of modular components comprising at least a directive component specifying a task;
2. validating structural integrity of said components according to predefined rules;
3. evaluating quality of assembled context across multiple dimensions including clarity, specificity, and actionability;
4. assembling said components into an optimized prompt structure;
5. executing said prompt against a selected language model provider; and
6. returning a normalized response object.

1. a modular component architecture for structuring contextual information;
2. a quality metrics engine for evaluating prompt effectiveness before execution;
3. a provider abstraction layer for unified execution across multiple language model providers;
4. a template system for encapsulating cognitive reasoning patterns; and
5. a transformation engine for converting contexts between different frameworks.

Dependent Claims

1. analyzing clarity by detecting vague language and ambiguous references;
2. measuring specificity by quantifying concrete versus abstract content;
3. assessing actionability by identifying imperative verbs and success criteria;
4. evaluating structure by analyzing logical organization and formatting; and
5. computing an overall quality score as a weighted combination of said dimensions.

1. a guidance component specifying role, behavioral rules, expertise areas, and communication style;
2. a constraints component defining boundaries, format requirements, and quality criteria; and
3. a sources component containing reference materials with relevance scoring.

1. a base template class with parameterized context generation;
2. a plurality of specialized templates for different cognitive patterns;
3. automatic parameter validation; and
4. built-in quality optimization for each template type.

ABSTRACT

A system and method for assembling, validating, and optimizing contextual information (prompts) for Large Language Models through a modular architecture comprising directive, guidance, constraints, and sources components. The invention includes a quality metrics engine that automatically evaluates prompt effectiveness across multiple dimensions, a provider abstraction layer enabling unified execution across different LLM providers, and a template system encapsulating 50+ cognitive reasoning patterns. The system enables developers to programmatically construct high-quality prompts, evaluate them before execution, and switch between providers without code changes, significantly improving consistency, reliability, and reusability of AI-powered applications.

DRAWINGS

Figure 1: System architecture for context assembly.

Figure 2: Quality metrics evaluation flow.

Figure 3: Template system hierarchy.

Figure 4: Provider abstraction layer.

Figure 5: Session management flow.

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: /Dhiraj Kumar Pokhrel/   Date: February 11, 2026

Printed Name: Dhiraj Kumar Pokhrel