🎯 Understanding AI Agent Grounding in LM-Kit.NET

📄 TL;DR

AI Agent Grounding is the practice of anchoring agent responses in factual, verifiable information rather than relying solely on the model's training data. Grounded agents retrieve relevant context from knowledge bases, documents, APIs, and real-time data sources before generating responses, dramatically reducing hallucinations and ensuring accuracy. In LM-Kit.NET, grounding is achieved through RAG (Retrieval-Augmented Generation), document attachments, tool-based information retrieval, and context injection.

📚 What is Agent Grounding?

Definition: Agent Grounding connects AI responses to external, verified sources of truth. Instead of generating answers purely from model weights (which can be outdated or incorrect), grounded agents:

Retrieve relevant information from trusted sources
Reference specific documents, data, or facts
Cite sources for transparency and verification
Refuse to answer when information isn't available

Why Grounding Matters

Without grounding, agents suffer from:

Hallucinations: Confident but incorrect statements
Outdated knowledge: Training data has a cutoff date
Lack of specificity: Generic answers instead of precise facts
No accountability: Cannot trace answers to sources

Grounded agents provide:

Factual accuracy: Answers backed by real data
Transparency: Clear source attribution
Freshness: Access to current information
Domain expertise: Specialized knowledge integration

The Grounding Spectrum

+---------------------------------------------------------------------------+
|                        Agent Grounding Spectrum                           |
+---------------------------------------------------------------------------+
|                                                                           |
|  Ungrounded                                                    Grounded   |
|      |                                                             |      |
|      v                                                             v      |
|  +--------+   +--------+   +--------+   +--------+   +--------+           |
|  | Model  |   | System |   |  RAG   |   | Tool   |   | Full   |           |
|  |  Only  |-->| Prompt |-->|        |-->| Access |-->|Context |           |
|  |        |   |Context |   |        |   |        |   |        |           |
|  +--------+   +--------+   +--------+   +--------+   +--------+           |
|                                                                           |
|  "What I     "Here's      "Search for  "Call APIs  "All sources           |
|   learned"    context"     relevant     for live    combined"             |
|                            documents"   data"                             |
|                                                                           |
+---------------------------------------------------------------------------+

🏗️ Grounding Mechanisms

1. RAG (Retrieval-Augmented Generation)

The most powerful grounding technique that retrieves relevant documents before generating:

using LMKit.Model;
using LMKit.Agents;
using LMKit.Retrieval;
using LMKit.Embeddings;

var model = LM.LoadFromModelID("gemma3:12b");
var embeddingModel = LM.LoadFromModelID("qwen3-embedding:0.6b");

// Create knowledge base
var dataSource = new DataSource(new EmbeddingGenerator(embeddingModel));

// Index documents
await dataSource.AddDocumentAsync("company_policies.pdf");
await dataSource.AddDocumentAsync("product_catalog.docx");
await dataSource.AddDocumentAsync("faq_database.json");

// Create grounded agent
var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("""
        You are a customer service assistant.
        Answer questions using ONLY the provided context.
        If information isn't in the context, say "I don't have that information."
        Always cite which document your answer comes from.
        """)
    .WithRag(dataSource, retrievalCount: 5)
    .Build();

var response = await agent.ExecuteAsync(
    "What is the return policy for electronics?",
    CancellationToken.None
);

2. Document Attachment

Ground responses in specific documents:

using LMKit.Agents;
using LMKit.Data;

var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("Answer questions based on the provided document.")
    .Build();

// Attach document for grounding
var attachment = new Attachment("quarterly_report.pdf");

var response = await agent.ExecuteAsync(
    new AgentRequest
    {
        Message = "What was the revenue growth this quarter?",
        Attachments = new[] { attachment }
    },
    CancellationToken.None
);

3. Tool-Based Grounding

Use tools to fetch real-time information:

using LMKit.Agents;
using LMKit.Agents.Tools.BuiltIn;

var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("""
        You are a research assistant with access to web search.
        Always search for current information before answering.
        Cite your sources in the response.
        """)
    .WithTools(tools =>
    {
        tools.Register(BuiltInTools.WebSearch);
        tools.Register(BuiltInTools.Http);
    })
    .Build();

// Agent will search for current data before responding
var response = await agent.ExecuteAsync(
    "What is the current stock price of Apple?",
    CancellationToken.None
);

4. Context Injection

Provide explicit context in the conversation:

using LMKit.Agents;

var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("Answer based on the provided context.")
    .Build();

// Inject context directly
var contextualPrompt = $"""
    CONTEXT:
    {GetRelevantContext(userQuery)}

    USER QUESTION:
    {userQuery}

    Answer based ONLY on the context above.
    """;

var response = await agent.ExecuteAsync(contextualPrompt, CancellationToken.None);

📊 Grounding Strategies

Strategy Comparison

Strategy	Use Case	Pros	Cons
RAG	Large knowledge bases	Scalable, semantic search	Requires indexing
Document Attachment	Specific documents	Simple, direct	Limited scope
Tool Access	Real-time data	Current info	API dependencies
Context Injection	Dynamic context	Flexible	Manual management
Hybrid	Complex applications	Best coverage	More complexity

Hybrid Grounding Architecture

+---------------------------------------------------------------------------+
|                       Hybrid Grounding Architecture                       |
+---------------------------------------------------------------------------+
|                                                                           |
|                           +--------------+                                |
|                           |  User Query  |                                |
|                           +------+-------+                                |
|                                  |                                        |
|                                  v                                        |
|  +--------------------------------------------------------------------+   |
|  |                        Context Assembly                            |   |
|  +--------------------------------------------------------------------+   |
|  |                                                                    |   |
|  |   +-----------+  +-----------+  +-----------+  +-----------+       |   |
|  |   |    RAG    |  |  Current  |  |   Tool    |  |  Static   |       |   |
|  |   | Retrieval |  | Document  |  |  Results  |  |  Context  |       |   |
|  |   +-----+-----+  +-----+-----+  +-----+-----+  +-----+-----+       |   |
|  |         |              |              |              |             |   |
|  |         +--------------+--------------+--------------+             |   |
|  |                                  |                                 |   |
|  |                                  v                                 |   |
|  |                       +-----------------+                          |   |
|  |                       | Merged Context  |                          |   |
|  |                       +-----------------+                          |   |
|  |                                                                    |   |
|  +--------------------------------------------------------------------+   |
|                                  |                                        |
|                                  v                                        |
|                           +--------------+                                |
|                           |   Grounded   |                                |
|                           |   Response   |                                |
|                           +--------------+                                |
|                                                                           |
+---------------------------------------------------------------------------+

🎯 Implementing Effective Grounding

Best Practices

using LMKit.Agents;
using LMKit.Retrieval;

var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("""
        GROUNDING INSTRUCTIONS:

        1. ALWAYS search the knowledge base before answering
        2. ONLY use information from retrieved documents
        3. If no relevant documents are found, say "I don't have information about that"
        4. CITE sources by mentioning the document name
        5. DISTINGUISH between facts from documents and your reasoning
        6. If information conflicts, present both viewpoints with sources
        7. NEVER make up statistics, dates, or specific claims

        RESPONSE FORMAT:
        - State your answer clearly
        - Reference the source: "According to [document name]..."
        - If uncertain, express the uncertainty
        """)
    .WithRag(knowledgeBase, retrievalCount: 5, minRelevanceScore: 0.7f)
    .Build();

Citation and Attribution

using LMKit.Retrieval;

// Retrieve with source tracking
var results = await dataSource.SearchAsync(query, topK: 5);

var contextWithCitations = string.Join("\n\n", results.Select((r, i) =>
    $"[Source {i + 1}: {r.DocumentName}]\n{r.Content}"
));

var prompt = $"""
    SOURCES:
    {contextWithCitations}

    QUESTION: {userQuery}

    Answer the question using the sources above. Cite sources using [Source N] notation.
    """;

Handling Missing Information

var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("""
        When you cannot find information in the provided context:

        1. Clearly state: "Based on my available sources, I don't have information about [topic]"
        2. Suggest what information might help answer the question
        3. Offer to help with related topics you DO have information about

        NEVER:
        - Make up information to fill gaps
        - Provide outdated information from training data
        - Guess at specific numbers, dates, or facts
        """)
    .WithRag(knowledgeBase)
    .Build();

📖 Key Terms

Grounding: Anchoring AI responses in verifiable external information
Hallucination: Generating plausible but factually incorrect content
RAG: Retrieval-Augmented Generation - combining retrieval with generation
Context Window: The text provided to the model for each inference
Citation: Attribution of information to its source
Knowledge Base: A searchable collection of trusted documents
Semantic Search: Finding documents by meaning rather than keywords

DataSource: Knowledge base for RAG
EmbeddingGenerator: Vector embeddings for semantic search
Attachment: Document attachment for context
TextChunker: Document chunking for indexing

RAG (Retrieval-Augmented Generation): Core grounding technique
Embeddings: Vector representations for semantic search
Vector Database: Storage for embeddings
AI Agent Memory: Persistent context across sessions
Context Windows: Token limits for context

🌐 External Resources

RAG Paper (Lewis et al., 2020): Original RAG research
Self-RAG (Asai et al., 2023): Self-reflective retrieval
RAGAS (Es et al., 2023): RAG evaluation framework
LM-Kit RAG Demo: Building grounded chatbots

📝 Summary

AI Agent Grounding transforms agents from unreliable answer generators into trustworthy, fact-based assistants. By retrieving relevant context before responding, grounded agents provide accurate, verifiable, and current information. In LM-Kit.NET, grounding is achieved through RAG (semantic search over document collections), document attachments (specific file context), tool-based retrieval (APIs and web search), and context injection (explicit information). Effective grounding combines clear instructions to cite sources, handle missing information gracefully, and never fabricate facts, enabling agents that users can trust for critical applications.

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