How to Use DeepSeek to Search and Verify Information

Choose the right way to use DeepSeek

If your goal is fact-checking, start with the web or app to test your process and prompts. Once the workflow is stable — for example, automatically turning an article into a table of claims to verify — move it into an API-based internal tool or document pipeline.

A practical DeepSeek fact-checking workflow

A safe workflow is to let DeepSeek do the reading, sorting and structuring, while you make the final judgement after opening the sources. Use this five-step process:

1. Define the scope. Give DeepSeek the topic, language, time period, geography, type of information you need and the output format you want.
2. Ask for checkable claims. Each claim should include a subject, action, time frame, place, number or quotation where relevant.
3. Ask what kind of source is needed. Depending on the claim, that might mean an official document, original report, legal text, public dataset, company statement or a news article that links to primary evidence.
4. Open the sources before drawing a conclusion. Do not cite the model’s answer as proof. Use only information you have matched to a source you can read.
5. Log the channel and model when reproducibility matters. The API docs state that the API version differs from the app/web version, so record whether you used web, app or API if the result may need to be audited later.

Starter prompt:

I need to research and verify information about: [topic].
Break the issue into checkable factual claims.
For each claim, list: what must be verified, the type of source I should open, suggested search queries, and the risk of relying only on AI reasoning.

Prompt templates for finding sources and checking claims

The more structured your question, the easier the output is to verify. Instead of asking, Is this topic true?, ask DeepSeek for a table, checklist or claim-by-claim breakdown.

1. Generate search queries

Create 10 search queries to verify this topic: [topic].
Group them by: official sources, original reports/data, journalism, and criticism or opposing views.
For each query, explain what type of result I should prioritise opening.

2. Extract claims from a paragraph

Read the text below and list the factual claims that could be true or false.
For each claim, identify: who, did what, when, any number or quotation involved, and the type of source needed before publication.

3. Compare two sources

I will paste two excerpts from two different sources.
Identify: where they agree, where they conflict, what lacks dates or context, which statements are interpretation, and which statements need verification against an original source.

4. Turn findings into a publication checklist

Turn the claims above into a checklist with these columns:
Claim | Source to open | Verification status | Risk | Editorial note.
Do not mark anything true or false unless a direct source is available.

Using the DeepSeek API for automated workflows

If you want to put DeepSeek into a chatbot, website, internal research tool or document-processing workflow, use the Open Platform/API instead of copying and pasting by hand. DeepSeek’s API docs say the API is compatible with the OpenAI API format; with configuration changes, you can use the OpenAI SDK or software compatible with the OpenAI API, using https://api.deepseek.com or https://api.deepseek.com/v1 as the base_url.

DeepSeek also provides documentation for Authentication and Create Chat Completion, the API used to generate chat responses. A basic Python skeleton looks like this:

from openai import OpenAI

client = OpenAI(
    api_key='DEEPSEEK_API_KEY',
    base_url='https://api.deepseek.com'
)

response = client.chat.completions.create(
    model='deepseek-chat',
    messages=[
        {'role': 'system', 'content': 'You help turn content into checkable factual claims.'},
        {'role': 'user', 'content': 'Break the following text into claims, source types to check, and verification risks: ...'}
    ]
)

print(response.choices.message.content)

Treat that code as a starting point, not a production-ready implementation. Before deploying it, check the official API documentation for the current authentication method, endpoint, parameters and model names.

deepseek-chat or deepseek-reasoner?

The API docs state that deepseek-chat and deepseek-reasoner correspond to DeepSeek-V3.2, have a 128K context limit and differ from the app/web version. The docs also describe deepseek-chat as non-thinking mode and deepseek-reasoner as reasoning mode.

A simple rule of thumb:

• Use deepseek-chat for fast summaries, classification, drafting, translation and straightforward checklists.
• Use deepseek-reasoner when you need multi-step analysis, comparison of arguments or a more complex reasoning chain.
• Log the model name if results need to be reproduced or audited, because the API and app/web versions are not the same.

Data, terms and compliance checks

If you use DeepSeek for work — especially through the API — review the terms and your organisation’s internal policies before sending sensitive, confidential or customer data. DeepSeek’s Terms of Use call user-provided data Inputs and model responses Outputs, including text, tables and code.

The terms also say DeepSeek may use technical measures to review user behaviour for legal and compliance purposes, including risk-filtering mechanisms. For the Open Platform, the terms require users to ensure that both they and their end users comply with DeepSeek’s Terms of Use. If you integrate DeepSeek into a product used by others, review the data flow, what gets sent to the API and your compliance responsibilities before launch.

Pre-publication checklist

Before using DeepSeek-assisted output in an article, report or public document, ask:

• Does every number, date, name and quotation have a direct source?
• Is any model inference being written as if it were an established fact?
• Are you relying on the most appropriate original or primary source available?
• Did you record whether the result came from DeepSeek web, app or API if reproducibility matters?
• Is the data you sent to DeepSeek consistent with the terms, internal policies and any obligations to end users?

Bottom line

The safest way to use DeepSeek for research and verification is to separate assistance from evidence. Let the model help you read faster, summarise, generate search queries and build checklists. Then verify the facts against original sources you open directly. Everyday users can begin with DeepSeek Chat or the app from the official site , while developers can integrate through the OpenAI-compatible API. For high-stakes content, turn every AI answer into a list of claims to check before you publish.