Integrating GSCP Prompting in a C# Application: A Practical Guide

Introduction

Gödel’s Scaffolded Cognitive Prompting (GSCP) brings structured reasoning, traceable decision-making, and confidence-based inference to LLM-based systems. In this article, we implement GSCP inside a C# application, making your app’s AI behavior intent-aware, auditable, and self-correcting.

Use Case: AI-Powered Career Guidance

Imagine a career platform offering services like resume help, Mock Interviews, Certification, and more. A user might say.

"Can you help me with my resume or maybe match me to a job?"

A simple keyword match is insufficient. Using GSCP, we extract, rank, and resolve user intent through eight structured reasoning steps, then utilize the JSON output to drive application logic.

Step 1. Constructing the GSCP Prompt

To start, we craft a structured GSCP-aware prompt string in C#. This prompt defines the input (user query, recommended service), the reasoning layers, and the required output in JSON format. It guides the LLM to perform structured analyses, such as normalization, sentiment evaluation, service matching, and confidence ranking.

🧾 C# Code to Build the Prompt

This prompt is a structured instruction for a language model to interpret a user's message using Gödel’s Scaffolded Cognitive Prompting (GSCP), a framework designed for layered, traceable reasoning. It guides the AI through specific cognitive steps: normalizing the input, extracting service-related keywords, analyzing emotional tone, generating multiple intent hypotheses with confidence scores, and selecting the best match or requesting clarification if ambiguity exists. This method enables the AI to make decisions that are not only accurate but also explainable and auditable, crucial in production environments.

Ultimately, the prompt instructs the model to return a clean, structured JSON object that contains its interpretation, the selected service, a confidence score, an explanation, and a memory trace for downstream use. This design facilitates parsing the output in a C# application and enables robust control flow based on the AI's reasoning, ensuring that decisions are both understandable to developers and aligned with user expectations.

string gscpPrompt = $@"
You are a highly intelligent AI assistant using Gödel’s Scaffolded Cognitive Prompting (GSCP).
Inputs:
- userMessage: '{userMessage}'
- availableServices: [{string.Join(", ", availableServices)}]
- recommendedService: '{recommendedService}'

GSCP Stages:
1. Normalize and extract service mentions (e.g., resume, jobs, mock interview)
2. Analyze sentiment and tone (confident, hesitant, enthusiastic)
3. Build multiple intent hypotheses with confidence scores
4. Score hypotheses based on match quality and tone
5. Select best matching service or return clarification if ambiguous
6. Log memory trace: selected service, confidence, rationale
7. Return a JSON object only with fields:
   - IsRelated
   - resolvedService
   - replyToUser
   - confidence
   - explanation
   - memoryTrace
";

This prompt drives the GSCP flow and instructs the model to return clean JSON output with interpretive traceability.

Step 2. Calling the LLM from C#

We now send the GSCP prompt to your LLM of choice (e.g., Azure OpenAI or OpenAI API) using a simple async method. The model will evaluate the prompt through GSCP steps and return a structured result.

⚙️ Code to Call the AI Service

string response = await _chatService.ChatMeAsync(
    gscpPrompt,
    0.1f,
    token
);

This response should be a JSON string representing GSCP’s final decision, including the resolved service, confidence score, and a reasoning summary.

Step 3. Parsing the LLM JSON Result

Once the LLM returns the JSON, we parse it into a strongly typed C# object. This allows us to easily access each GSCP output field, such as confidence or reply message, and trigger the appropriate logic in the application.

📦 C# Class and Parsing Code

public class GscpResult
{
    public string IsRelated { get; set; }
    public string ResolvedService { get; set; }
    public string ReplyToUser { get; set; }
    public double Confidence { get; set; }
    public string Explanation { get; set; }
    public Dictionary<string, string> MemoryTrace { get; set; }
}
var result = JsonConvert.DeserializeObject<GscpResult>(response);

This turns the JSON result into a structured GscpResult object that can drive application flow.

Step 4. Executing Business Logic Based on GSCP

Now that we’ve parsed the result, we route the user to the correct service or ask for clarification if the model is unsure. This logic ensures safe and explainable user experiences, especially important in regulated or customer-facing domains.

🧭 Branching Logic for Service Resolution

if (result.IsRelated == "yes" && result.Confidence > 0.8)
{
    RouteToService(result.ResolvedService);
    ShowMessage(result.ReplyToUser);
}
else
{
    AskForClarification();
}

This guards against acting on weak matches or ambiguous inputs, a key principle in GSCP reasoning.

Step 5 (Optional). Maintain GSCP Memory Across Sessions

To fully leverage GSCP’s “memory trace” feature, we can log decision history in session or persistent storage. This allows future prompts to adapt based on prior interactions, such as reinforcing user preferences or avoiding repetition.

💾 C# Code to Track GSCP History in Session

List<GscpResult> sessionTrace = 
    HttpContext.Session.Get<List<GscpResult>>("GSCP_TRACE") ?? new();

sessionTrace.Add(result);
HttpContext.Session.Set("GSCP_TRACE", sessionTrace);

Memory traces can also be used for debugging, UX personalization, or compliance audits.

Summary

By embedding GSCP into a C# application, you gain

• ✅ Structured reasoning across 7–8 AI layers
• ✅ Explainable, auditable AI logic
• ✅ Intent resolution with high confidence control
• ✅ JSON integration into strongly typed systems
• ✅ Memory trace for adaptive UX

What’s My Next Article?

• 🔁 Use GSCP for full conversational agents
• 🧠 Expand into ML-powered decision support
• 📊 Log and visualize memory traces in dashboards
• 🚀 Generate GSCP prompts dynamically from API context or UI metadata