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Practice what you just read
10 free practice questions. No credit card.
| API | Use Case | Key Details |
|---|---|---|
| InvokeModel | Single synchronous call | Returns full response; not streaming |
| InvokeModelWithResponseStream | Streaming response | Returns tokens as generated; lower perceived latency |
| Converse | Multi-turn conversation | Unified API across models; handles message history |
| ConverseStream | Streaming multi-turn | Converse + streaming combined |
| InvokeAgent | Bedrock Agent invocation | Multi-step task orchestration with tools |
| Retrieve | Knowledge Base RAG query | Returns chunks without generating a response |
| RetrieveAndGenerate | Knowledge Base RAG + LLM | Retrieves chunks and generates grounded response |
| Factor | Considerations |
|---|---|
| Capability | Reasoning, coding, multilingual, vision, long context |
| Cost | Price per 1K input/output tokens; On-demand vs Provisioned |
| Latency | Time-to-first-token; critical for real-time UX |
| Context window | How much text the model can process at once |
| Modalities | Text-only vs text+image vs text+image+document |
| Fine-tunability | Whether the model supports fine-tuning in Bedrock |
Practice what you just read
10 free practice questions. No credit card.
| Technique | Description | Best For |
|---|---|---|
| Zero-shot | Task description only, no examples | Simple, well-understood tasks |
| Few-shot | 2–10 labeled examples in prompt | Tasks requiring format or style guidance |
| Chain-of-thought (CoT) | Prompt model to reason step-by-step | Multi-step reasoning, math, logic |
| System prompt | Sets persona, constraints, tone | Consistent behavior across turns |
| Retrieval augmentation | Inject relevant docs into context | Grounding responses in factual data |
| ReAct | Reason + Act interleaved | Agentic workflows with tool calls |