What is RAG? A Practical Approach to Letting Your Own Knowledge Answer
RAG (Retrieval-Augmented Generation) is a mechanism that makes a generative AI (LLM) search through in-house documents and knowledge, and uses the results as the basis to generate answers. It is more attractive because it is faster, cheaper, and safer to create a company-specific AI than retraining the model.
In short, RAG is composed of two steps:
- Retrieval: Search for fragments of internal data related to the question
- Generation: Use the found fragments as evidence for the LLM to generate the answer
The key is that the areas where LLMs tend to "pretend to know" (policies, product specifications, contracts, FAQs, procedures, etc.) are where RAG works best. Conversely, it is not suitable for applications that require accurately answering information that does not exist in internal data.
The Big Picture: The Standard RAG Architecture
A typical RAG flow looks like this:
- Data collection (PDF / Confluence / Notion / SharePoint / Google Drive / DB, etc.)
- Preprocessing (OCR, remove unnecessary parts, normalization, metadata tagging)
- Chunking (split text into suitably sized chunks)
- Embeddings (vectorize chunks)
- Store in Vector DB (make searchable)
- Search (semantic search + filtering, re-ranking if needed)
- Prompt composition (feed the search results as evidence to the LLM)
- Answer generation (format citations, evidence, notices)
- Evaluation & Monitoring (accuracy, hallucination, leakage, cost, latency)
Tip 1 for Avoiding Failures: Define Use Cases by Question Type
RAG is not a universal solution, so defining the question type at the outset increases success rate. A common approach is to classify into three types as follows:
- Search-type: Find the relevant portion and summarize (e.g., "What are the rules for expense reimbursement?")
- Procedure-type: Break down internal procedures into steps (e.g., "What is the initial response during an outage?")
- Decision-support-type: Use policies or specs as evidence to branch by condition (e.g., "Is this case eligible for a return?")
Starting with the "Search-type" is a classic approach. It's easy to evaluate and helps gain internal trust.
Tip 2 for Avoiding Failures: Data Preparation Should Not Be Overdone for AI
It's common to think "perfectly cleanse the data first...," but for RAG it pays to start small with high-value data. Preferred priorities are as follows:
- FAQs and inquiry history (the questions themselves become search keys)
- Regulations and manuals (clear evidence and easy to evaluate)
- Product specifications and release notes (the more often updated, the more effective RAG)
- Minutes and chat logs (lots of noise, so defer)
If you have many PDFs, OCR quality is the bottleneck. If scan PDFs are included, first ensure that you can extract text.
Chunk Design: The Subtle but Important Factor That Affects Accuracy
How you cut the text into chunks can significantly affect search accuracy. As a starting point, consider the following:
- Chunk length: about 300–800 tokens (for Japanese, roughly 600–1600 characters)
- Overlap: 50–150 tokens (to reference across paragraphs)
- Unit: use headings (h2/h3) or article clauses (Article ○) to separate; this helps
Documents with structure like regulations or specifications benefit from metadata such as headings + clause numbers + revision date, which improves downstream filtering and makes the system much smarter.
Improve Search Quality: Vector Search plus Alpha Works Well in the Field
When RAG isn't delivering, the cause is usually search. Here's a collection of practical improvements for real-world use.
1) Hybrid Search (Keyword + Vector)
Vector search is good for paraphrase/semantic similarity, but for information where exact matches matter, such as part numbers or clause numbers, keyword search is strong. Combining both as a "hybrid search" is becoming standard.
2) Re-ranking
Take the top candidates from the initial search and reorder them with a separate model to decide which are truly relevant. It tends to improve accuracy, but adds cost and latency, so it's common to narrow from, for example, top 20 to top 5.
3) Metadata Filters
Filtering by "department", "product", "year", "document type" etc. reduces noise dramatically. For example, human resources policies should include applying company and revision version.
Prompt Design: The Key is “Make the Evidence Do the Talking”
In RAG, simply pasting the retrieved documents can lead the LLM to fill in gaps with convenient assumptions. The recommended approach is:
- Do not make assertions beyond the evidence (if evidence is missing, ask to clarify / say "unknown")
- Citations (which document and which part) are mandatory
- Ask clarifying questions when the intent is unclear
Prompt Example (Policy)
You are an assistant that answers based on company internal knowledge. Build conclusions only from the given "evidence" and do not make guesses beyond the evidence. Be sure to include citations of the evidence (document name and section) in your answer. If evidence is insufficient, provide the missing information and ask clarifying questions.
Tooling: Start by Prioritizing “Speed to Build”
RAG implementations can be built from scratch, but nowadays there are many options. Here are common configurations (you can tailor to your needs as needed):
- Orchestration : LangChain / LlamaIndex
- Vector DB : Pinecone / Weaviate / Milvus / PostgreSQL (pgvector)
- Search (keyword) : Elasticsearch / OpenSearch
- Evaluation : RAGAS, custom test sets + automatic scoring
- Monitoring : Logging foundation for prompts, search queries, hit documents, answers, costs, latency
For small-scale trials, pgvector + simple app-side implementation is plenty effective. As data volume and concurrency grow, many teams move to a dedicated vector DB.
How to Create Evaluations: An Evaluation Set is the Best Asset for RAG
To iterate improvements, you need an evaluation Q&A set close to your internal questions. As a rule of thumb, 30–100 items already provide substantial value.
Focus indicators are three:
- Retrieval quality: Whether the correct evidence appears in the top K (e.g., Top-5 hit rate)
- Answer quality: Whether the conclusion is correct and not lacking or excessive
- Evidence consistency: Whether citations truly support the conclusion (detection of fabricated citations)
In internal reviews, people will ask, "Where in this document is the answer?" Therefore, it's recommended to early on include evaluation of citation design and evidence consistency.
Security and Access Control: In RAG, the worst thing is exposing information that should not be visible
The real danger in internal RAG is not hallucinations but exposure of information beyond access rights. The solution is simple: start with the following:
- Reflect ACLs in search: filter search results according to user permissions
- Masking: preprocess personal data, confidential numbers, etc.
- Logging: keep track of who asked what and what was returned
In particular, designing so that, at ingestion time, the vector data is readable by anyone is prone to accidents. Always include document-level permission metadata and build a filtering structure from the start.
Common Pitfalls and Remedies
- Answers are too vague: Chunks are too short, resulting in insufficient evidence / not enforcing citations → revisit chunk design and enforce citations
- Picking up irrelevant documents: Metadata is weak / no hybrid search → apply filters by document type, department, product and use keywords in tandem
- Not reflecting the latest updates: No update pipeline → incremental ingestion (delta indexing) and maintain revised-date metadata
- High cost: Too much noise in search / long-contexts → reduce Top-K, narrow with re-ranking, then summarize before feeding
Fast-track Roadmap (4-Week Schedule)
Week 1: Pick the Target Business Area and Data
- Choose one area with many inquiries (e.g., internal IT helpdesk)
- Gather about 50–200 data items (FAQs and procedures are ideal)
Week 2: Get the Minimum RAG Running
- Chunking + embeddings + store in vector DB
- Search top-5 → LLM answer with citations
Week 3: Improve Search Accuracy
- Hybrid search, metadata discipline
- Create an evaluation set of 30–50 questions and visualize improvements
Week 4: Build the Operational Foundation
- Set up permissions (ACL), logging, and update pipelines
- Run a closed internal pilot and use question logs for next improvements
Conclusion: RAG as a Project to Make Your Internal Knowledge Usable
Building RAG is more about data design, search design, and evaluation design than about choosing a model. The smaller you start and the more you grow question logs and evaluation sets, the smarter the AI becomes. First, pick a commonly asked question and start with answering it with citations, and we'll progress together.
