Chat vs RAG vs Agent
When should I use a simple chatbot vs RAG vs an autonomous agent?
Three progressively complex approaches to AI applications. Start simple, add complexity only when needed.
Approaches
Simple Chat (Direct API Call)
simpleSend user message to LLM, get response back. No retrieval, no tools, no planning.
Pros
- Extremely simple to implement (5-20 lines of code)
- Lowest latency - single API call
- Lowest cost - no additional infrastructure
- Easy to debug and understand
Cons
- Limited to model's training data (knowledge cutoff)
- Can't access real-time information
- No access to your proprietary data
- May hallucinate when asked about specifics
Use When
- General Q&A where the model's training data is sufficient
- Creative writing, brainstorming, ideation
- Code generation for common patterns
- Summarization of user-provided text
Avoid When
- Users need current information (news, prices, weather)
- Answers must reference your proprietary documents
- High accuracy is required for specific facts
- The task requires taking actions in external systems
Show Code Example
const response = await openai.chat.completions.create({
model: 'gpt-4o',
messages: [
{ role: 'system', content: 'You are a helpful assistant.' },
{ role: 'user', content: userMessage }
]
});
return response.choices[0].message.content;RAG (Retrieval Augmented Generation)
moderateRetrieve relevant documents from a vector database, include them in the prompt, then generate a response grounded in that context.
Pros
- Grounds responses in your actual documents
- Reduces hallucination with source attribution
- Can handle proprietary/private data
- Knowledge can be updated without retraining
Cons
- Requires vector database infrastructure
- Quality depends on chunking and retrieval
- Higher latency (embedding + search + generation)
- Can retrieve irrelevant passages
Use When
- Answering questions about your documentation
- Customer support with knowledge bases
- Legal/compliance research on internal documents
- Any Q&A where accuracy and citations matter
Avoid When
- You don't have documents to retrieve from
- Questions are general knowledge (just use chat)
- Sub-second latency is critical
- The task requires taking actions, not just answering
Show Code Example
// 1. Embed the query
const embedding = await embed(userQuery);
// 2. Search vector database
const relevantDocs = await vectorDB.search(embedding, { topK: 5 });
// 3. Generate with context
const response = await openai.chat.completions.create({
model: 'gpt-4o',
messages: [
{ role: 'system', content: 'Answer based on the provided context.' },
{ role: 'user', content: `Context:\n${relevantDocs.join('\n')}\n\nQuestion: ${userQuery}` }
]
});
return response.choices[0].message.content;Agent (Autonomous with Tools)
complexAn LLM that can reason, plan, use tools, and take actions in a loop until the task is complete.
Pros
- Can accomplish multi-step tasks autonomously
- Can use external tools and APIs
- Handles complex reasoning and planning
- Can take actions, not just provide information
Cons
- Significantly more complex to build and debug
- Higher latency and cost (multiple LLM calls)
- Can get stuck in loops or make mistakes
- Requires careful guardrails and supervision
Use When
- Tasks require multiple steps or tools
- User needs actions taken, not just answers
- Complex research requiring multiple sources
- Workflow automation with decision-making
Avoid When
- A simple answer would suffice
- Latency is critical (users won't wait)
- The task is well-defined with known steps (just script it)
- You can't tolerate occasional failures or loops
Show Code Example
const agent = new Agent({
model: 'gpt-4o',
tools: [searchWeb, readFile, sendEmail, createTicket],
maxIterations: 10
});
// Agent will plan, execute tools, and iterate
const result = await agent.run(
'Research competitor pricing and create a summary report'
);
// Result includes: final answer, actions taken, reasoning traceDecision Factors
| Factor | Simple Chat (Direct API Call) | RAG (Retrieval Augmented Generation) | Agent (Autonomous with Tools) |
|---|---|---|---|
| Data source Where does the answer come from? | Use for general knowledge in model's training data | Use when answers must come from your documents | Use when answers require combining multiple sources or APIs |
| Action required Does the user need information or action? | Information only, no external actions | Information only, but grounded in your data | Actions required (file operations, API calls, etc.) |
| Latency tolerance How long can the user wait? | Best for <2s response requirements | Acceptable for 2-5s response times | Only for tasks where users expect to wait |
| Accuracy requirements How critical is factual accuracy? | Acceptable for casual use, creative tasks | Better for factual queries with source citation | Use when task verification is possible |
Real-World Scenarios
Customer asks 'What is your return policy?' on support chat
Answer must come from your actual policy documents, and you want to cite the source.
User asks 'Write me a poem about coffee'
Creative task with no external data needs. RAG or agents add latency with no benefit.
User says 'Research our competitors and create a report'
Multi-step task requiring web search, analysis, and document creation. Simple approaches can't handle this.
Internal tool: 'Answer questions about our codebase'
Answers must be grounded in your actual code. Embed your codebase and retrieve relevant snippets.
Common Misconceptions
Quick Decision Guide
Start with the simplest approach. Only add complexity (RAG, then agents) when you have a concrete reason. Most use cases don't need agents.