Pick the right DSPy module and architecture for your AI feature. Use when you are not sure whether to use Predict, ChainOfThought, ReAct, or a pipeline, need to choose between DSPy patterns, want architecture advice for your AI feature, or are deciding between a single module and a multi-step pipeline. Also use for which DSPy module should I use, Predict vs ChainOfThought, when to use ReAct, single module vs pipeline, DSPy architecture decision, CoT vs PoT vs ReAct, do I need a pipeline, module selection guide, DSPy pattern selection, how to structure my DSPy program.
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Pick the right DSPy module and architecture for your AI feature. Use when you are not sure whether to use Predict, ChainOfThought, ReAct, or a pipeline, need to choose between DSPy patterns, want architecture advice for your AI feature, or are deciding between a single module and a multi-step pipeline. Also use for which DSPy module should I use, Predict vs ChainOfThought, when to use ReAct, single module vs pipeline, DSPy architecture decision, CoT vs PoT vs ReAct, do I need a pipeline, module selection guide, DSPy pattern selection, how to structure my DSPy program.
Choose the Right DSPy Architecture
When NOT to use this skill
Already know what module to use — go to the matching /dspy-* skill
Fixing errors in existing code — use /ai-fixing-errors
Learning a specific module — use the matching /dspy-* skill
Need a project plan, not an architecture decision — use /ai-planning
Step 1: Answer 3 questions
Before recommending anything, get answers to these three questions from the user (or infer them from context):
What goes in and what comes out? Input type and format, output type and format.
Does the AI need external tools? Search, APIs, databases, calculators, code execution?
How complex is the reasoning? Simple mapping, moderate analysis, complex multi-step logic?
Step 2: Pick the module
Walk the decision tree:
Does it need tools?
├── Yes: Does it need to write and run code?
│ ├── Yes → CodeAct
│ └── No → ReAct
└── No: How complex is the reasoning?
├── Simple (direct mapping) → Predict
├── Moderate (needs explanation) → ChainOfThought
├── Complex (math/computation) → ProgramOfThought
└── Very complex (compare approaches) → MultiChainComparison
Module tradeoff summary:
Module
Accuracy
Latency
Cost
Best for
Predict
Baseline
1x
1x
Simple classification, extraction, formatting
ChainOfThought
+10-30%
1.5-2x
1.5-2x
Most tasks — default choice when unsure
ProgramOfThought
+20-40% on math
2-3x
2-3x
Math, computation, data manipulation
ReAct
Varies
3-10x
3-10x
Tasks requiring external information or actions
CodeAct
Varies
3-10x
3-10x
Tasks requiring code generation and execution
MultiChainComparison
+5-15%
3-5x
3-5x
When you need the best possible single answer
BestOfN
+5-10%
Nx
Nx
When you have a reward function and acceptance threshold
For the full module list including Refine, RLM, and Parallel, see reference.md.
Step 3: Single module vs pipeline
Use this table to decide whether one module is enough or a pipeline is warranted:
Signal
Single module
Pipeline
Input maps directly to output
Yes
--
Task has distinct phases (classify then generate)
--
Yes
Different parts need different LM capabilities
--
Yes
Need to validate intermediate results
--
Yes
Simple input-output with clear signature
Yes
--
Need to combine retrieval + generation
--
Yes
Rule of thumb: start with a single module. Add pipeline stages only when you have measured a quality gap that a single module cannot close.
Verification: After implementing the chosen architecture, run dspy.Evaluate(devset, metric=your_metric) on 20-50 examples to confirm the module choice was correct before optimizing.
Step 4: Architecture-to-optimizer pairing
Architecture
First optimizer
Best optimizer
Why
Single Predict
BootstrapFewShot
MIPROv2
Simple, fast to optimize
Single ChainOfThought
BootstrapFewShot
MIPROv2
Reasoning benefits from good demos
ReAct agent
BootstrapFewShot
BootstrapFewShot
Agents are hard to optimize, start simple
Multi-module pipeline
BootstrapFewShot
MIPROv2
End-to-end optimization tunes all stages
Pipeline with fine-tuning
BootstrapFinetune
BetterTogether
Weight tuning for max quality
Step 5: Generate the recommendation
Output the recommendation in this format:
## Architecture Recommendation
**Module:** dspy.ChainOfThought (or whatever was chosen)
**Why:** [1-2 sentences tying the module to the task]
**Skeleton:**
[minimal code showing the module or pipeline structure]
**Optimizer path:**
1. Start with BootstrapFewShot (quick baseline)
2. Move to MIPROv2 if accuracy needs to improve
**Alternative considered:** [what else was considered and why it was not chosen]
Defaulting to ChainOfThought for everything. Predict is better for simple classification or extraction where reasoning adds noise, not signal. If the correct output is a fixed label from a known set, CoT can hallucinate reasoning that leads it astray.
Using ReAct when a pipeline suffices. ReAct is for tasks that need dynamic tool selection at runtime. If you know the steps upfront (e.g., always retrieve then answer), use a pipeline — it is cheaper, faster, and easier to optimize.
Over-engineering with MultiChainComparison. MCC runs 3-5x the cost of a single pass. Only reach for it after measuring that single-pass accuracy is insufficient for your use case.
Building a pipeline before proving a single module works. Always start with the simplest module that could work. Measure it on your eval set. Add pipeline stages only when you have a specific, measured quality gap.
Ignoring cost implications early. A ReAct agent with 10 tool calls costs roughly 10x a single Predict call. Factor cost and latency into architecture decisions before you build, not after.
Cross-references
Install any skill: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill <name>
For full module comparison tables and complete code templates, see reference.md
For worked architecture decisions with real examples, see examples.md
Ready to build? Use the matching /dspy-* skill for your chosen module
Need to implement a pipeline? Use /ai-building-pipelines
Want to plan the full project? Use /ai-planning
Need to review existing code? Use /ai-auditing-code
Install /ai-do if you do not have it — it routes any AI problem to the right skill and is the fastest way to work: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-do
