Table of Contents

🎯 Understanding Prompt Engineering in LM-Kit.NET


📄 TL;DR

Prompt Engineering is the practice of designing and optimizing inputs to language models to achieve desired outputs. Rather than modifying model weights (fine-tuning), prompt engineering works at the inference layer by crafting effective system prompts, user messages, few-shot examples, and reasoning instructions. In LM-Kit.NET, prompt engineering is fundamental to all text generation, extraction, and agent operations, implemented through ChatHistory, AuthorRole, system prompts, and guidance parameters across the SDK.


📚 What is Prompt Engineering?

Definition: Prompt Engineering is the art and science of constructing inputs that guide language models toward producing accurate, relevant, and useful outputs. It encompasses:

  • Instruction design: Clear, unambiguous task descriptions
  • Context management: Providing relevant background information
  • Output formatting: Specifying desired structure and style
  • Reasoning guidance: Encouraging step-by-step thinking

Why Prompt Engineering Matters

+-------------------------------------------------------------------------+
|                     The Prompt Engineering Impact                       |
+-------------------------------------------------------------------------+
|                                                                         |
|  SAME MODEL + DIFFERENT PROMPTS = VASTLY DIFFERENT RESULTS              |
|                                                                         |
|  +---------------------------+     +---------------------------+        |
|  |     Poor Prompt           |     |     Engineered Prompt     |        |
|  +---------------------------+     +---------------------------+        |
|  | "Summarize this"          |     | "Summarize this article   |        |
|  |                           |     |  in 3 bullet points,      |        |
|  |                           |     |  focusing on key findings |        |
|  |                           |     |  for a technical audience"|        |
|  +---------------------------+     +---------------------------+        |
|              |                                  |                       |
|              v                                  v                       |
|  +---------------------------+     +---------------------------+        |
|  | Vague, unfocused output   |     | Precise, actionable output|        |
|  | Wrong length or format    |     | Correct format and tone   |        |
|  | May miss key points       |     | Covers essential points   |        |
|  +---------------------------+     +---------------------------+        |
|                                                                         |
+-------------------------------------------------------------------------+

Prompt Engineering vs Fine-Tuning

Aspect Prompt Engineering Fine-Tuning
Model modification None Adjusts weights
Setup time Minutes Hours to days
Compute cost Zero Significant
Flexibility Change anytime Requires retraining
Domain adaptation Through context Through data
Best for General tasks Specialized domains

🔍 Core Prompting Techniques

1. Zero-Shot Prompting

Direct instruction without examples:

using LMKit.TextGeneration;

var chat = new MultiTurnConversation(model);

// Zero-shot: just describe the task
var response = chat.Submit(
    "Classify the following text as positive, negative, or neutral: " +
    "'The product arrived on time but the quality was disappointing.'",
    CancellationToken.None
);
// Output: "negative"

2. Few-Shot Prompting

Providing examples to guide the model:

var systemPrompt = """
    You are a sentiment classifier. Given a text, respond with exactly one word:
    positive, negative, or neutral.

    Examples:
    Text: "I love this product!"
    Sentiment: positive

    Text: "Worst experience ever."
    Sentiment: negative

    Text: "It arrived yesterday."
    Sentiment: neutral
    """;

var chat = new MultiTurnConversation(model);
chat.SystemPrompt = systemPrompt;

var response = chat.Submit(
    "Text: 'The service was okay, nothing special.'\nSentiment:",
    CancellationToken.None
);
// Output: "neutral"

3. Chain-of-Thought (CoT) Prompting

Encouraging step-by-step reasoning:

var systemPrompt = """
    You are a helpful assistant that solves problems step by step.
    Always explain your reasoning before giving the final answer.
    Format:
    1. Break down the problem
    2. Work through each step
    3. State your final answer clearly
    """;

var chat = new MultiTurnConversation(model);
chat.SystemPrompt = systemPrompt;

var response = chat.Submit(
    "A store has 45 apples. If 3/5 of them are sold and then 12 more arrive, " +
    "how many apples does the store have?",
    CancellationToken.None
);
// Output includes reasoning steps before the answer

4. Role-Based Prompting

Assigning a persona to guide behavior:

var systemPrompt = """
    You are an experienced senior software engineer specializing in C# and .NET.
    When reviewing code:
    - Focus on performance, security, and maintainability
    - Provide specific, actionable suggestions
    - Reference best practices and design patterns
    - Be constructive but thorough
    """;

var chat = new MultiTurnConversation(model);
chat.SystemPrompt = systemPrompt;

5. Structured Output Prompting

Requesting specific output formats:

var systemPrompt = """
    Extract information and return it as JSON with this exact structure:
    {
        "name": "string",
        "email": "string or null",
        "phone": "string or null",
        "company": "string or null"
    }
    Return ONLY the JSON, no additional text.
    """;

⚙️ Prompt Engineering in LM-Kit.NET

ChatHistory and AuthorRole

LM-Kit.NET structures conversations using ChatHistory and AuthorRole:

using LMKit.TextGeneration;

var chat = new MultiTurnConversation(model);

// System prompt sets the context and behavior
chat.SystemPrompt = "You are a helpful coding assistant.";

// User messages are the inputs
var response = chat.Submit("How do I read a file in C#?", CancellationToken.None);

// The conversation history maintains context
// Assistant responses are automatically added to history

System Prompts in Agents

using LMKit.Agents;

var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("""
        You are a research assistant with access to web search.

        INSTRUCTIONS:
        1. Always search for current information before answering
        2. Cite your sources with URLs
        3. If information conflicts, present both viewpoints
        4. Clearly distinguish facts from opinions

        RESPONSE FORMAT:
        - Start with a brief summary
        - Provide detailed findings
        - End with source citations
        """)
    .WithTools(tools => tools.Register(BuiltInTools.WebSearch))
    .Build();

Guidance in Extraction

using LMKit.Extraction;

var extractor = new TextExtraction(model);

// Guidance provides task-specific context
extractor.Guidance = """
    This is a US tax form (W-2).
    - Dates are in MM/DD/YYYY format
    - Dollar amounts may include commas as thousand separators
    - Employer Identification Numbers (EIN) are in XX-XXXXXXX format
    - Social Security Numbers should be extracted as XXX-XX-XXXX
    """;

extractor.Elements.Add(new TextExtractionElement("employer_name", ElementType.String));
extractor.Elements.Add(new TextExtractionElement("wages", ElementType.Double));

Categorization Descriptions

using LMKit.TextAnalysis;

var categorizer = new Categorization(model);

// Descriptions guide classification decisions
categorizer.Categories.Add("Technical Support",
    "Questions about product functionality, bugs, errors, or how to use features");
categorizer.Categories.Add("Billing Inquiry",
    "Questions about charges, payments, subscriptions, refunds, or invoices");
categorizer.Categories.Add("Sales",
    "Interest in purchasing, pricing, demos, or product comparisons");

📊 Prompt Design Patterns

The CRISP Framework

+-------------------------------------------------------------------------+
|                      CRISP Prompt Framework                             |
+-------------------------------------------------------------------------+
|                                                                         |
|  C - Context     : Background information and domain                    |
|  R - Role        : Who the model should act as                          |
|  I - Instructions: What task to perform                                 |
|  S - Specifics   : Constraints, format, length requirements             |
|  P - Provide     : Examples, data, or reference material                |
|                                                                         |
+-------------------------------------------------------------------------+

Example CRISP Implementation

var systemPrompt = """
    CONTEXT:
    You are helping a customer service team at an e-commerce company.
    The company sells electronics and has a 30-day return policy.

    ROLE:
    Act as a senior customer service representative with 10 years experience.
    Be professional, empathetic, and solution-oriented.

    INSTRUCTIONS:
    1. Acknowledge the customer's concern
    2. Identify the issue type (return, refund, exchange, complaint)
    3. Provide a clear resolution path
    4. Offer additional assistance

    SPECIFICS:
    - Keep responses under 150 words
    - Use a warm but professional tone
    - Never promise things outside policy
    - Escalate complex issues to supervisors

    EXAMPLES:
    Customer: "My laptop arrived broken"
    Response: "I'm so sorry to hear your laptop arrived damaged. That's certainly
    not the experience we want for you. Since it's within our 30-day window, I can
    arrange a free return and send a replacement right away. Would you prefer a
    replacement or a full refund?"
    """;

🎯 Best Practices

Do's

  1. Be specific and explicit

    // Good
"List exactly 5 benefits, each in one sentence"

// Poor
"List some benefits"

  2. Use delimiters for clarity

    var prompt = """
    Summarize the following article:

    ---BEGIN ARTICLE---
    {articleContent}
    ---END ARTICLE---

    Summary:
    """;

  3. Specify output format

    "Respond in JSON format with keys: summary, key_points, sentiment"

  4. Include negative instructions

    "Do NOT include any personally identifiable information in your response."

Don'ts

  1. Avoid ambiguity

    // Ambiguous
"Make it better"

// Clear
"Improve clarity by simplifying complex sentences and adding transition words"

  2. Don't overload with instructions

    • Keep prompts focused on one primary task
    • Break complex tasks into multiple steps

  3. Avoid conflicting instructions

    • Review prompts for contradictions
    • Prioritize instructions clearly

📖 Key Terms

  • System Prompt: Instructions that define the model's behavior and context
  • User Message: The input or query from the user
  • Few-Shot Learning: Providing examples within the prompt to guide behavior
  • Chain-of-Thought (CoT): Prompting technique that encourages step-by-step reasoning
  • Zero-Shot: Prompting without examples, relying only on instructions
  • Role Prompting: Assigning a persona to influence response style
  • Prompt Template: Reusable prompt structure with placeholders
  • Guidance: Task-specific context that improves extraction accuracy

📚 Related API Documentation


🔗 Related Glossary Topics


🌐 External Resources


📝 Summary

Prompt Engineering is the practice of crafting effective inputs to guide language model outputs without modifying model weights. Core techniques include zero-shot (direct instructions), few-shot (learning from examples), chain-of-thought (step-by-step reasoning), and role-based prompting. In LM-Kit.NET, prompt engineering is implemented through system prompts in MultiTurnConversation and AgentBuilder, guidance parameters in TextExtraction, and category descriptions in Categorization. Following the CRISP framework (Context, Role, Instructions, Specifics, Provide examples) helps create effective prompts. Good prompt engineering dramatically improves output quality, relevance, and consistency across all LLM applications.