What is Grounding?
Grounding in AI is the practice of connecting an AI model's outputs to verified, factual sources of information -- such as company databases, documents, or trusted external sources -- to ensure responses are accurate, current, and traceable rather than generated from the model's training data alone.
What Is Grounding in AI?
Grounding is the process of anchoring AI outputs to verified information sources rather than relying solely on what the model learned during training. When an AI model is "grounded," it references specific, up-to-date data to support its responses, much like a responsible employee who cites sources and checks facts rather than speaking purely from memory.
Without grounding, AI models generate responses based on patterns in their training data, which can be outdated, incomplete, or simply wrong. This is where hallucinations occur -- the AI produces confident-sounding responses that are factually incorrect because it has no connection to current, verified information.
Grounding solves this by connecting the AI to real-time, authoritative data sources before it generates a response. The AI retrieves relevant facts first, then crafts its answer based on those facts rather than its general knowledge alone.
Why Grounding Matters
The single biggest concern business leaders have about deploying AI is accuracy. An AI chatbot that gives customers incorrect product information, a document summarizer that invents facts, or an analysis tool that cites nonexistent statistics can damage trust, create liability, and undermine the entire AI initiative.
Grounding directly addresses this concern by:
- Reducing hallucinations significantly (studies show 40-80 percent reduction depending on the approach)
- Ensuring AI responses reflect your company's current data, not outdated training information
- Making AI outputs verifiable by linking responses to specific source documents
- Keeping responses current as your data changes, without retraining the model
How Grounding Works
The most common grounding technique is Retrieval-Augmented Generation (RAG), which works as follows:
- User submits a query: "What is our return policy for electronics?"
- Retrieval step: The system searches your company's knowledge base and finds the current return policy document
- Context injection: The relevant document content is provided to the AI model alongside the user's question
- Grounded generation: The AI generates a response based on the retrieved document rather than its general training
- Source citation: The response includes a reference to the source document, enabling verification
Other grounding approaches include:
- Tool use and function calling: The AI can query databases, APIs, or calculation engines to get real-time data before responding
- Knowledge graph integration: Connecting the AI to structured knowledge representations that encode facts and relationships
- Search augmentation: Using web search or enterprise search to find current information relevant to the query
Business Applications
Customer Service Grounded AI chatbots answer customer questions using your actual product documentation, pricing, and policies rather than making up answers. When a customer asks about warranty terms, the chatbot retrieves and quotes your actual warranty document.
Internal Knowledge Management Employees can ask questions about company processes, HR policies, or technical procedures and get accurate answers grounded in your actual documentation, with links to the source documents for verification.
Financial Analysis AI tools grounded in your financial data can generate reports, answer questions about performance, and identify trends based on real numbers rather than general knowledge about your industry.
Regulatory Compliance For businesses in regulated industries, grounded AI ensures that responses about compliance requirements reference actual regulations and company policies rather than generating plausible but potentially incorrect guidance.
Implementing Grounding
A practical grounding implementation requires:
- A knowledge base: Your documents, databases, and data sources organized for retrieval
- An embedding and search system: To find relevant information quickly (typically using vector databases and semantic search)
- An AI model with retrieval capabilities: Many modern AI platforms support grounding natively (Google Vertex AI, AWS Bedrock, Azure OpenAI Service all offer built-in grounding features)
- Source citation in outputs: Configuring the system to show users where information came from
Relevance for Southeast Asian Businesses
Grounding is especially important for ASEAN businesses because:
Multilingual accuracy: AI models may have weaker knowledge about Southeast Asian markets, regulations, and business practices. Grounding with your local, current information compensates for this gap.
Regulatory environments: Different ASEAN countries have distinct regulations for data protection, employment, trade, and financial services. Grounded AI ensures responses reflect the specific regulations relevant to each market rather than generic information.
Building trust: In markets where AI adoption is still growing, businesses that deploy grounded AI build trust faster by providing verifiably accurate information. Customers and employees are more likely to rely on AI tools that cite their sources.
Practical first step: If you are deploying any customer-facing AI, grounding should be considered a requirement, not an option. Start by identifying the documents and data sources that the AI needs access to, organize them in a searchable format, and use a RAG-enabled AI platform to connect the model to your information.
Grounding is the critical technique that makes AI trustworthy enough for business use. Without it, AI models can hallucinate and provide incorrect information that damages customer trust and creates liability. With proper grounding, AI becomes a reliable tool that provides accurate, verifiable, and current answers based on your actual business data.
- Treat grounding as a requirement rather than an optional feature for any customer-facing or decision-support AI application -- ungrounded AI creates unacceptable risks of misinformation
- Invest in organizing and maintaining the knowledge base that grounds your AI, because the quality of AI responses is directly limited by the quality and currency of the source documents it retrieves
- Choose AI platforms with built-in grounding and RAG capabilities (Google Vertex AI, AWS Bedrock, Azure OpenAI) to simplify implementation rather than building retrieval infrastructure from scratch
Frequently Asked Questions
What is the difference between grounding and fine-tuning?
Fine-tuning changes the AI model itself by training it on new data, permanently embedding knowledge into the model's parameters. Grounding does not change the model -- instead, it provides relevant information to the model at query time so it can generate accurate responses. Grounding is more flexible because you can update the source documents without retraining the model. It is also more transparent because you can see exactly what information the model used. Most businesses should start with grounding rather than fine-tuning, as it is simpler, cheaper, and more maintainable.
Does grounding completely eliminate AI hallucinations?
Grounding significantly reduces hallucinations but does not eliminate them entirely. A well-implemented grounding system typically reduces hallucinations by 40-80 percent compared to an ungrounded model. The AI can still occasionally misinterpret retrieved information or fail to retrieve the most relevant documents. This is why human review remains important for high-stakes outputs. The best approach combines grounding with clear instructions to the AI to say "I don't have information about that" when retrieved documents do not adequately address the question.
More Questions
Treat your AI knowledge base like any other critical business system -- it needs regular updates and maintenance. Establish a process for updating documents when policies, products, or procedures change. Set up automated pipelines to re-index updated documents so the AI always retrieves current information. Assign ownership for knowledge base accuracy to specific team members. Most businesses find that monthly review cycles are sufficient for relatively stable information, while pricing, inventory, and policy documents may need more frequent updates.
Need help implementing Grounding?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how grounding fits into your AI roadmap.