About RAG Technology - The Mechanism for Achieving High-Precision Responses
RAG (Retrieval-Augmented Generation) is the core technology that enables SmartWeb’s AI chatbots and AI email response creation to generate high-precision responses.
What is RAG?
Problems with Traditional AI
Traditional Large Language Models (LLMs) had the following issues:
| Problem | Description |
|---|---|
| Hallucinations | Fabricating information that seems plausible but doesn’t exist in training data |
| Outdated Information | Only possessing information from the training period, unable to handle latest information |
| Lack of Specific Information | Not knowing information specific to your company’s products and services |
Solution through RAG
RAG solves these problems by combining “retrieval” and “generation”.
The key point is pre-registering and indexing knowledge. When AI receives a question, it searches for relevant information within this pre-registered knowledge and generates responses based on that information. It does not search websites in real-time.
flowchart TB
A(Customer Question) --> B(Search Pre-registered Knowledge)
B --> C(Retrieve Relevant Information)
C --> D(Generate Response Based on Information)
D --> E(Output Accurate Response)
style A fill:#ffffcc
style B fill:#cce5ff
style C fill:#cce5ff
style D fill:#ffcccc
style E fill:#ccffcc
Figure: RAG Processing Flow (Yellow: Input, Blue: Retrieval Phase, Pink: Generation Phase, Green: Output)
Note: “Search” refers to searching within knowledge sources pre-registered in FlowHunt, not internet search.
How RAG Works
Step 1: Understanding the Question
When a customer inputs a question, the AI analyzes the question’s intent and extracts keywords and concepts suitable for search.
Step 2: Searching Pre-registered Knowledge (Retrieval)
Searches for information related to the question from pre-registered and indexed FlowHunt knowledge sources (Schedules, Q&A, Documents).
Important: This search does not search websites or external documents on the internet in real-time. It only searches within knowledge pre-registered in FlowHunt.
- Vector Search: High-speed search for semantically similar content
- Keyword Search: Search by specific terms or product names
- Hybrid Search: High-precision search combining both approaches
Step 3: Context Construction
Organizes the relevant information found through search and constructs the context necessary for response generation.
Step 4: Response Generation (Generation)
The LLM generates natural and accurate responses based on the search results.
Important: Since the LLM responds based on “search results” rather than “its own knowledge,” hallucinations are significantly reduced.
Benefits of RAG
1. Prevention of Hallucinations
| Traditional AI | RAG-equipped AI |
|---|---|
| Responds by inferring from training data | Responds based on search results |
| May generate non-existent information | Responds “I don’t know” when information isn’t in knowledge base |
| Unclear basis for responses | Can identify response sources |
2. Support for Latest Information
By updating the knowledge base, AI responses are immediately updated. No need for LLM retraining.
3. Support for Company-specific Information
By registering product manuals, FAQs, internal documents, etc., in the knowledge base, it can accurately respond to company-specific questions.
4. Cost Efficiency
Compared to LLM fine-tuning (additional training), RAG is efficient in the following aspects:
| Item | Fine-tuning | RAG |
|---|---|---|
| Information Updates | Requires retraining | Knowledge update only |
| Cost | High | Low |
| Reflection Speed | Days to weeks | Immediate |
| Expertise Required | Yes | No |
RAG Utilization in SmartWeb
Supported Features
| Feature | RAG | Description |
|---|---|---|
| AI Chatbot | ✓ | Uses RAG for automated customer responses |
| AI Email Response Creation (Composer) | ✓ | Uses RAG for generating email reply drafts |
| AI Response Assist (Improver) | - | Text improvement only (no search) |
Importance of Knowledge Sources
Since RAG searches from “pre-registered knowledge,” it cannot respond to information that hasn’t been registered. Therefore, RAG accuracy heavily depends on knowledge base quality:
- Comprehensiveness: Does it cover all frequently asked questions?
- Accuracy: Is the information accurate and up-to-date?
- Clarity: Are the texts clear and easily understood by AI?
For details, see “How AI Learns”.
RAG Limitations and Countermeasures
Limitation 1: Questions about Unregistered Information
Since RAG searches from pre-registered knowledge, it cannot respond to information that hasn’t been registered. It cannot retrieve external information on the spot like internet search.
Countermeasures:
- Set fallback responses like “Please contact us through the inquiry form”
- Utilize escalation features to human operators
Limitation 2: Impact of Search Accuracy
Response quality degrades when appropriate information cannot be found through search.
Countermeasures:
- Optimize knowledge base structure
- Create content considering synonyms and similar terms
- Cover important question patterns comprehensively in Q&A
Limitation 3: Complex Reasoning
Complex reasoning combining multiple pieces of information is challenging.
Countermeasures:
- Escalate complex questions to human operators
- Pre-register anticipated question patterns in Q&A
Summary
Through RAG technology, SmartWeb’s AI achieves the following:
| Feature | Effect |
|---|---|
| High Precision | Accurate responses based on knowledge |
| Timeliness | Immediate reflection through knowledge updates |
| Customization | Support for company-specific information |
| Reliability | Significant reduction in hallucinations |
To maximize RAG effectiveness, knowledge base quality management is important. We recommend regular content review and updates.
Related Information
- How AI Learns - Knowledge source setup and optimization
- About FlowHunt - The platform that enables RAG
- AI Chatbot Response Accuracy - Key points for improving accuracy
