What to choose: RAG, fine-tuning, or prompt engineering?
Tool for selecting the best AI architecture for your use case. Analyze data freshness, corpus size, latency, personalization, and budget to get a motivated recommendation.
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Frequently Asked Questions
When should you choose RAG instead of fine-tuning?
BART is preferred when data changes frequently (news, prices, updated documents often), when the corpus is very large (difficult to compress into a training set), and when you don't want to retrain every time the data changes. BART retrieves up-to-date information at query time, eliminating the problem of "cutoff dates" in models. Fine-tuning is better when you need to deeply modify the style, tone, or behavior of the model on stable data.
What is the difference between RAG, fine-tuning and prompt engineering?
Prompt engineering optimizes instructions given to the model without modifying weights: simple, cost-effective but limited in capacity. Fine-tuning modifies the model's weights on specific data, adapting style and behavior permanently but requiring GPU and labeled data. RAG (Retrieval Augmented Generation) combines a document database with the model: retrieves relevant pieces and inserts them into the prompt in real-time, keeping data separate from the model and always up-to-date.
Can fine-tuning replace RAG entirely?
No, and vice versa. They are complementary: many production systems use both (fine-tuning for adapting style/behavior + RAG for updated data). Fine-tuning cannot "remember" large bodies of updatable facts without overfitting and risk of hallucinations. RAG cannot change the model's style or teach it new behaviors. The choice depends on the primary problem: updating data (RAG) vs personalizing behavior (FT).
How much does it cost to implement a RAG system versus fine-tuning?
Prompt engineering has almost zero cost (only API call). RAG requires a vector store (Chroma, pgvector, Redis Stack, Qdrant - free or ~$20-50/month for SaaS), indexing process and retrieval pipeline: typical development cost is 2-4 weeks. Fine-tuning with LoRA on GPU A100 costs $50-500 per training run (on cloud) or requires dedicated GPUs on-prem; MLOps pipeline adds 4-8 weeks of development. Both require ongoing maintenance.
Does your RAG work with open-source models on-prem?
Yes, RAG is independent of the model and works optimally with open-source on-prem models (Llama, Mistral, Qwen via Ollama, llama.cpp or vLLM) combined with self-hosted vector stores (pgvector on PostgreSQL, Redis Stack, Chroma). This architecture - 100% on-prem, zero cloud, zero recurring API costs - is particularly suited for companies requiring data privacy. The retriever can use sentence-transformers for free embeddings or pre-trained model embedding models.
How is it used?
- Assess the freshness of your data
Indicate if the data the model uses changes frequently (e.g., real-time prices, news) or is stable (technical documentation, consolidated corporate knowledge base).
- Specify the characteristics of the corpus
Estimate dataset size for proprietary data. Small datasets can be included directly in the prompt, while large ones require retrieval (RAG) or training (fine-tuning).
- Define latency and customization
Indicate the latency budget (real-time vs batch) and how much you need to personalize the model's behavior compared to the baseline. Tight latency and deep customization favor fine-tuning.
- Get a recommendation
Click "Analyze AI Architecture" to get the recommendation RAG/Fine-tuning/Prompt Engineering with explanation of key factors for your use case.
What architecture of AI should I use - fine-tuning or prompt engineering? How to choose the right one.
One of the most critical decisions in AI system development is choosing between three main approaches to adapt a LLM to a specific domain: prompt engineering (optimizing instructions without touching the model), fine-tuning (modifying model weights on proprietary data), and RAG - Retrieval Augmented Generation (retrieving relevant information from an external database at query time).
There is no universal answer: the optimal choice depends on five key factors. Data freshness is often the most discriminant factor: if information changes frequently (prices, regulations, evolving corporate knowledge base), RAG is almost always superior because it retrieves updated data at query time without having to retrain. Fine-tuning "freezes" knowledge in training data, becoming obsolete quickly.
Fine-tuning excels when you need to deeply modify the model's style, tone, or behavior - for example, to create an assistant that responds with your company's specific terminology, uses a precise legal style, or generates code in a proprietary framework. For small corpora (few hundred documents) within the context window, few-shot prompt engineering is often the fastest and most cost-effective solution.
This recommendation is based on community ML heuristic (MEDIUM confidence): scores reflect consolidated best practices but do not replace a thorough technical evaluation of your specific case. Hybrid architectures (fine-tuning + RAG) are common in production. Calculations occur entirely in the browser without data transmission.
Practical example: AI assistant for an enterprise knowledge base
- Case: Internal AI assistant for 50,000 updated business documents weekly.
- Data freshness: Dynamic (weekly updates) - Score RAG +9
- Corpus size: Large (more than 50,000 documents won't enter the context window) → score RAG increased by +6
- Latency: Moderate (<2s acceptable for internal assistant) → Score RAG +3
- Personalization: Low (standard style is acceptable) → Rating RAG favorite score
Dictionary - Rag, Fine-Tuning and Prompt Engineering
- Ragtag
- Retrieval Augmented Generation: architecture combining a vector database (for semantic search) with an LLM. At query time, it retrieves the most relevant fragments from the corpus and injects them into the prompt. The data remains separate from the model and always updatable without retraining.
- Fine-tuning
- Further training of a pre-trained model on specific domain data. Adjusts model weights to adapt style, tone, specialized knowledge. Requires GPU, labeled dataset, and MLOps pipeline. Common techniques: LoRA, QLoRA, SFT (Supervised Fine-Tuning), DPO.
- Model Training and Deployment
- Optimization of instructions provided to the model without modifying its weights. Techniques: zero-shot, few-shot (examples in the prompt), chain-of-thought, system prompts. Affordable and immediate but limited for complex tasks or those requiring deep personalization.
- Data Storage for Federico Calo's Projects
- Vector similarity search database. Used in RAG to retrieve the most relevant document chunks for a query. Self-hosted options: pgvector (PostgreSQL), Redis Stack (HNSW), Chroma, Qdrant. Cloud options: Pinecone, Weaviate Cloud.
- Embedding
- Vector numeric that represents the semantic meaning of a text. Embeddings are used by RAG to calculate similarity between queries and documents. Open-source models: sentence-transformers/all-MiniLM-L6-v2 (384 dim), BGE-M3 (1024 dim), Qwen-Embedding. Available on-prem with Ollama.
- Low-Rolling Average
- Low-Rank Adaptation: efficient fine-tuning technique that trains only a subset of parameters (low-rank matrices), drastically reducing GPU requirements. QLoRA adds model base quantization (4-bit) during training, making fine-tuning of models from 7B-70B feasible even on single 24GB GPUs.
Do you need a custom analysis?
This tool is free and informative. For in-depth analysis with AI on-prem - private data, zero cloud - contact Federico.