Why does DeepSeek need its own playbook?
Because of its dual nature: an official assistant with a large international user base, and open weights embedded in third-party products everywhere. Work done for DeepSeek transfers across that spillover. Read the mechanics at DeepSeek for global users before starting.
Step-by-step: the DeepSeek sequence
- Baseline in both modes (week 1). Run your fixed question set twice: once with web search enabled, once without. Log which mode produced which answer — the fixes differ completely. Screenshot everything; score the four dimensions.
- Fix retrieval fundamentals (weeks 1-3). Crawlability, clean HTML, fast responses. Retrieval mode can only cite what it can fetch. Audit your crawler posture per AI crawler management.
- Publish answer blocks (weeks 2-6). Your top buyer questions as self-contained passages: definition, dated number with source, implication. See answer block writing.
- Build the corroboration layer (weeks 3-10). Reviews, comparison articles, community mentions — grounding cross-checks claims against independent sources. See source graph building.
- Cover the Chinese-language fact layer (weeks 4-8). DeepSeek’s users prompt heavily in Chinese. Accurate Chinese-language brand pages — and consistent Chinese names, product names and descriptions across resellers and marketplaces — materially improve answer accuracy. See multilingual GEO.
- Invest in model knowledge (weeks 4-12, ongoing). Wikidata entry, consistent entity facts, authoritative coverage. This is the slow fix for what the model says with search off. See Wikipedia and Wikidata.
- Re-measure (week 12). Both modes, same prompts, same scoring, delta memo with screenshots.
The checklist
| Item | Standard | Status |
|---|---|---|
| Two-mode baseline | Search on + off, screenshots, 4-dimension scores | Week 1 |
| Retrieval fundamentals | Fetchable, fast, clean HTML | Weeks 1-3 |
| Answer blocks | Top-10 questions covered, dated sourced claims | Weeks 2-6 |
| Corroboration | Reviews + comparisons + community mentions | Weeks 3-10 |
| Chinese fact layer | Accurate zh pages, consistent naming | Weeks 4-8 |
| Model knowledge | Wikidata + consistent entity facts | Weeks 4-12 |
| Re-measurement | Both modes, delta memo | Week 12 |
Who should own DeepSeek work inside an export brand?
Ideally the same team that owns GEO measurement generally, with one addition: a Chinese-language reviewer. The most common DeepSeek failure for export brands is an inaccurate or absent Chinese-language fact layer, and catching it requires someone who can read what the model says in Chinese. If that capability does not exist in-house, it is the one skill worth contracting — a quarterly review of Chinese-language answers is enough for most brands.
How does DeepSeek fit the wider engine portfolio?
Treat it as part of a portfolio, not a special project. The fixed question set should include DeepSeek alongside ChatGPT, Gemini, Perplexity and Copilot, scored on the same four dimensions with the same evidence standard. Where DeepSeek differs is reporting: because stakeholders often do not know the engine, the delta memo should note not just what changed but why the surface matters — international users, open-weight spillover, bilingual buyer research. Haipengyun Holding Group’s domestic line covers DeepSeek alongside Doubao, Yuanbao, Kimi, Tongyi and Wenxin, which is why export programs include these engines by default rather than as exotic add-ons.
Common pitfalls
Do not judge DeepSeek on model-knowledge mode alone — stale training descriptions are fixed by months of entity work, not complaints. Do not skip the Chinese-language layer if any of your buyers prompt in Chinese. And do not ignore the open-source spillover: buyers may meet a DeepSeek-derived assistant inside a vertical tool, so this playbook covers more surfaces than one app. Run it once and it becomes part of your standard quarterly rhythm.
Where to go next
Review the DeepSeek engine page, the analysis at why export brands should care, and the outbound context at China outbound GEO.