DeepSeek V3.2 · workflow
DeepSeek's technical report shows that V3.2 uses large-scale environment and complex-instruction synthesis to train Agent generalization; when using it, organize tool results, task constraints, and verifiable outcomes into a trajectory instead of relying on a single “please think autonomously” instruction.
Build an evidence trace from {{SOURCE_PACKET}} for {{AGENT_TASK}}; attach {{SOURCE_ID}}, {{TIMESTAMP}}, and {{EXPECTED_OUTCOME}} to every {{TOOL_OUTPUT}}, then run {{VERIFY_COMMAND}}.Replace before running: {{SOURCE_PACKET}}, {{AGENT_TASK}}, {{SOURCE_ID}}, {{TIMESTAMP}}, {{EXPECTED_OUTCOME}}, {{TOOL_OUTPUT}}, {{VERIFY_COMMAND}}
Split long documents into an evidence packet with source IDs. After each tool return, record timestamp, citation location, and expected outcome before continuing. At the end, sample-check citations and run the verification command; mark claims without an original passage unknown and rerun with a smaller context. Training scale in the report is not API performance.
DeepSeek's technical report shows that V3.2 uses large-scale environment and complex-instruction synthesis to train Agent generalization; when using it, organize tool results, task constraints, and verifiable outcomes into a trajectory instead of relying on a single “please think autonomously” instruction.
Good for: Long-context research, coding Agents, search/tool chains, and multi-step tasks that require outcome verification.
Not good for: “Autonomous Agents” without reliable tool results or executable acceptance criteria; it is also not suitable to treat the training report as a complete deployment prompt specification.
Applicable model versions: DeepSeek-V3.2; the report also discusses V3.2-Speciale, and the two have different tool and length limits.
Applicable clients, Agents, or APIs: OpenAI-compatible APIs, and DeepSeek's proprietary/custom Agent harnesses.
Recommended reasoning tier and parameters: Fix thinking/non-thinking according to the task; for code and research, start with high as the baseline, then use evals to step down.
You are a verifiable multi-step Agent.
<goal>
Complete: <specific task>
</goal>
<constraints>
- Use only authorized tools and context.
- State the goal, input evidence, and verifiable result for each step.
- Do not access or write to unauthorized resources; stop and report when uncertain.
</constraints>
<workflow>
1. Break down the task and list the completion criteria.
2. Choose the minimum necessary tools, and save the parameters and return source for every call.
3. Update the plan based on tool results; if results conflict, preserve the conflict and verify again.
4. When finished, run tests/rule checks and compare each item against the completion criteria.
</workflow>
<final_format>
Conclusion: ...
Evidence: ...
Tool-trace summary: ...
Unresolved items: ...
</final_format>Run a no-tool baseline first, then provide the same tool set and an outcome checker.
Store each task's tool calls, reasoning state, results, and final outcome as a replayable trajectory.
For long-context tasks, test at input lengths such as 32k/64k/128k in separate tiers to determine whether the DSA/server-side implementation changes quality or latency.
Use a rule-based checker or human scoring to validate the final result, rather than merely counting whether long reasoning was generated.
The report says it uses a large-scale agentic task synthesis pipeline covering more than 1,800 environments and 85,000 complex prompts.
The report states that tool-use scenarios train both thinking and non-thinking expert domains, with the goal of improving generalization and instruction-following robustness in complex interactions.
The report describes V3.2 as being close to GPT-5 and Kimi-K2-Thinking on several reasoning benchmarks, while improving the capabilities of open models on agent tasks; these are the authors' experimental conclusions.
At the technical level, the report says DSA reduces the main attention complexity from O(L²) to O(Lk), using top-k selection in long-context use; this explains the long-context efficiency goal and does not mean that every API task will speed up linearly.
This prompt is a reusable workflow template based on the technical report, not a complete system prompt officially released by DeepSeek; it should be labeled as an adapted template.
1,800/85,000 is the scale of the training-data synthesis, not the success rate on public evaluation samples; data volume cannot be treated as proof of capability.
DSA's complexity depends on the server-side kernel, sequence length, and hardware; self-hosted reproduction must be evaluated using actual throughput and latency.
Agent outcomes require tools and checkers; the model itself does not guarantee that real verification will be executed.
The technical report calls the key method the “Large-Scale Agentic Task Synthesis Pipeline” (a compliance-limited short quote).
DeepSeek-V3.2 technical report (arXiv) · Source date: 2025-12-03 · Edited: 2026-09-20
Read the original sourceDeepSeek V3.2
Run this guide in the environment listed above. Downloading does not transfer the template or establish model availability for your account.