๐๏ธ Build the Knowledge Base for LLMs
Retrieval-Augmented Generation (RAG) has quickly become the go-to approach for connecting large language models (LLMs) to external knowledge bases. By retrieving chunks of information at query time, RAG ensures models can stay grounded in dynamic knowledge without retraining. RAG is not the only option. Depending on your needs of accuracy, latency, cost efficiency, or domain-specific reasoning, other approaches may be more suitable, or complementary.
Table of Contents
1. 7 Methods to Build LLM Knowledge Base
- Fine-Tuning / Domain Adaptation
What it is: Updating model weights with your knowledge base.
Best when:
- Knowledge base is stable (not updated daily).
- Need to internalize jargon, style, or reasoning patterns.
Drawbacks: Expensive to retrain frequently; inflexible with changing data.
- Adapters & LoRA (Low-Rank Adaptation)
What it is: Lightweight fine-tuning layers injected into the model.
Best when:
- Domain-specific adaptation is needed without full fine-tuning costs.
- You want iterative, lower-cost updates.
- Can be combined with RAG for retrieval grounding.
- Knowledge Graphs (KG) & Graph Neural Networks
What it is: Represent knowledge as a graph of entities and relations.
Best when:
- Knowledge is structured and relational.
- Need reasoning like โWhich suppliers connect to both X and Y?โ.
- Require consistency and explainability.
Challenges:Building and maintaining graphs is resource-intensive.
- In-Context Learning with Memory
What it is: Long-term memory (vector DB, episodic memory) the model can update/query.
Best when:
- Conversational agents need continuity and personalization.
- Knowledge evolves during interactions.
Challenges:Scaling memory and filtering relevance.
- Hybrid: RAG + Structured Tools
What it is: Combine retrieval with APIs, SQL, or KG lookups.
Best when:
- Part of KB is unstructured (docs, PDFs) and part structured (DBs, APIs).
- Need higher factual accuracy with authoritative sources.
- Pre-Computing & Indexing (Distillation)
What it is: Compress KB into synthetic training data and fine-tune/prompt-tune.
Best when:
- Latency is critical.
- KB fits into model limits after distillation.
Drawbacks: Hard to update; hallucination risk.
- Agentic Systems with Tool Use
What it is: LLM calls specialized tools (search engines, SQL, reasoning modules).
Best when:
- Tasks require computation + knowledge.
- E.g., โWhatโs the average downtime for servers in Q3?โ
Advantage: Enables dynamic reasoning and problem-solving.
โ Summary
- RAG โ best for dynamic, text-heavy KBs.
- Fine-tuning/LoRA โ best for stable, domain-specific KBs.
- KGs & hybrids โ best for structured reasoning.
- Agentic tool use โ expands beyond retrieval into active problem-solving.
2. Token Cost Efficiency
If you consider token cost efficiency rather than maximizing the performance, here is a table compare the token costs for different methods.
- Token Cost Comparison
| Method | Inference Token Cost | Training / Setup Cost | Update Flexibility | Best For |
|---|---|---|---|---|
| RAG | High (retrieved text inflates prompt) | Low | Very high (just re-index docs) | Dynamic/unstructured KB |
| Fine-Tune | Low (query + response only) | Very high | Low (expensive retraining) | Stable, domain-specific KB |
| LoRA/Adapters | Low | Medium | Medium (cheap retraining) | Semi-dynamic KB, budget-sensitive |
| KG | Very Low | High upfront | Medium (graph updates) | Structured knowledge, reasoning tasks |
Bottom line:
- If token cost matters most โ Fine-tuning, LoRA, or KG beat RAG.
- If knowledge updates often โ RAG is cheapest overall.
- If structured data dominates โ KG is the long-term token-efficient choice.
3. Using Google Gemini API / Vertex AI
In this section, I introduce 5 paths for building LLM knowledge base with Google Gemini API or Vertex AI.
- RAG
- Use Vertex AI RAG Engine or Vertex AI Search for enterprise document search.
- Optionally add Google Search grounding for web freshness + citations.
- Fine-Tuning
- Supported on Vertex AI Supervised Tuning.
- Not currently available in standalone Gemini API.
- LoRA / Adapters
- Managed via Vertex AI for open models (e.g., Gemma).
- Not directly exposed in Gemini API.
- Knowledge Graphs
- Stand up Neo4j/Graph DB; integrate via function calling.
- Compact, token-efficient structured facts.
- Agentic Tools
- Available via function calling for dynamic reasoning and computation.
4. TF-IDF: A Classic RAG Baseline
Before embeddings, TF-IDF (Term FrequencyโInverse Document Frequency) was the standard retrieval method.
Key idea: Highlight terms that are frequent in a document but rare across the corpus.
Applications:
- Document similarity & clustering
- Text classification (spam filtering, sentiment analysis)
- Keyword extraction
- Recommendation systems
๐ GeeksforGeeks TF-IDF tutorial
INFS247-Dr.Luo Chatbot
This is an RAG example that employs TF-IDF to retrieve Syllabus information in an Course AI Assistant:
5. Final Thoughts
RAG may dominate today, but itโs just one part of the ecosystem:
- Go RAG if KB updates frequently.
- Go Fine-tuning/LoRA for stable, domain-specific KBs.
- Go KG for relational reasoning.
- Go Hybrid/Tools for dynamic reasoning + computation.
๐ The future is RAG + fine-tuning + tools + structured data: a complementary stack, not a competition.
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