Deepseek prompt tips and qwen prompting guidance barely differ from advice for the paid frontier models, except in emphasis. Open and lower-cost models reward longer, more explicit instruction blocks, they benefit from having their reasoning made visible as a separate step, and they need more manual verification because they are more likely to produce fluent text that fails at the edges.
This article is written for Indian users who reach for these models because a paid plan is not worth it right now. Availability, pricing, and which app fronts which model change constantly, so treat that as a reason to check current plan pages, not as something this article can confirm for you.
What actually changes with these models
Three differences show up in practice, and they are worth separating because each has a different fix.
- Instruction following drops on long, implicit prompts. If the constraint is buried in the sixth sentence of a paragraph, it gets lost. Blocks and numbering survive better than prose.
- Longer context does not mean reliable retrieval. A model can accept a large document and still answer from the middle of it rather than from the part you cared about. Put the requirement near the end.
- Fluency stays high while accuracy falls. The tone does not degrade the way capability does, so a weak answer looks exactly as confident as a strong one.
The third point is why the verification steps below are not optional. Where these models are weak, they are weak quietly.
Write the instruction block out long, then number it
Task. [one sentence, one deliverable] Background, as facts only. 1. [fact] 2. [fact] 3. [fact] Constraints. 1. [constraint, such as no invented numbers] 2. [constraint, such as every recommendation needs a reason] 3. [constraint, such as maximum number of items] Output format. Return [table with named columns, or numbered list with named parts]. Nothing outside this format. Before you answer. List anything in my request that you could not fulfil, one line each, using [CONFIRM: what is needed]. If that list is not empty, still answer, but keep those items visibly unfinished rather than guessing.
Fill in these details
- Facts only in the background block: Anything you are unsure about belongs in the list of things to confirm, not in the background.
- Numbered constraints: One constraint per line. These models follow numbered lines noticeably better than they follow a paragraph of mixed instructions.
- Output format with named parts: Column names or part names, not "use a table".
- The unfinished-items rule: This is the single most useful line when you cannot verify everything the model will produce.
Keep a second, shorter version of the same prompt for quick one-off questions. Not every task needs nine lines of scaffolding.
Ask for the reasoning as a separate, visible step
Longer-running and step-oriented modes are common in this family of models, though exactly which features your app exposes changes with the version and the plan. Where a reasoning or step-by-step mode is available, use it and then apply this structure:
Step 1. Restate the task in one sentence and list what you need from me that is missing. Step 2. State your approach in at most five numbered lines. Step 3. Do the work. Step 4. Check your own answer against every constraint in my request, one line per constraint, marked pass or fail. Step 5. Give the final answer in the output format I specified, with no reasoning text.
Step 4 is the part people skip. Asking for a self-check against your own constraints converts a silent failure into a visible line you can argue with.
If your app has a separate toggle for thinking or reasoning, treat it as part of the prompt rather than a shortcut. Turning it on and then asking for a short final answer gives you a longer process and a shorter output.
Use a second pass as a critic
For anything that will be published, sent to a client, or used in a decision, run the output back through the same model as a reviewer. This works with open models because the second pass costs almost nothing on a free or low-cost tier.
Below is an answer you produced. Do not revise it yet. Review it against this checklist and report only problems: [constraint 1], [constraint 2], [constraint 3]. Flag any factual claim you cannot support from the material I gave you, any instruction you did not follow, and any sentence that says nothing specific. Quote the sentence for each problem. If you find no problems in a category, write "none found" for it. Answer to review: [paste output]
Then take the flagged items and decide. The second pass does not need to be right. It needs to be differently wrong.
Worked example: one prompt tuned for a long-context open model
A developer pastes a 40-page specification and asks for a migration plan. The default prompt fails twice: the requirement sits at the top of the document, and the answer is confidently wrong about one table in the middle.
The fix is position, not length.
Task. Produce a data migration plan from the legacy schema in the attached document to the new schema described in section [number].
Critical constraint. The legacy table [exact table name] is referenced in more than one place. Read every reference to it in the document before you plan, and list the pages or sections where you found it. If you find fewer than [number] references, stop and tell me which sections are missing.
Output format. A table with columns: old table, new table, row count estimate basis, transform rule, risk, and where in the document the rule comes from.
Before you answer. List any table in the new schema you could not find in the document. Do not assume a default mapping.
The "list the pages where you found it" instruction is doing the work. It turns retrieval into something checkable, and when it comes back short you find out before you trust the plan.
Check the output before you ship it
1. Verify the numbers, not the prose
Open models are fluent enough that prose reviews feel sufficient and are not. Pull every figure, name, and date out of the output and confirm each one against your source. Treat any number you cannot trace as invented.
2. Re-run with a different angle
Ask the same question in a different structure, such as "answer this as a checklist rather than a plan". Divergence between the two runs marks the parts that were guesswork. Agreement across two different framings is not proof, but it is a useful filter.
3. Confirm it followed the format
Check the output format line by line. If it drifted, add an explicit "nothing outside this format" and re-run rather than editing by hand, because the drift usually comes from a missing instruction elsewhere.
Common deepseek prompt tips that do not apply everywhere
- "Use very detailed prompts" with no structure. Detail without structure is just length. Numbered blocks beat paragraphs.
- Assuming long context means it read the middle. Check by asking it to quote from the section you care about.
- Trusting the tone as a signal. Confidence is not accuracy, and these models are consistent about that.
- Omitting the self-check. One extra step catches a surprising share of constraint violations.
- Comparing against paid models without fixing the prompt. A weaker model with a better-structured prompt often beats a stronger one with a vague one.
Frequently asked questions
Is a free model good enough for professional work?
For drafting, summarising, classification, and first-pass structure, often yes. For facts you cannot easily verify, anything that goes to a client, and anything where a wrong number has consequences, keep a human check in the loop regardless of the tier.
How do I prompt qwen and deepseek differently from each other?
Mostly you do not. Both respond best to the same block structure. Differences show up in reasoning modes, language coverage, and instruction sensitivity, so keep a short note per model and update it when you test.
Can I use these models on a phone?
Third-party apps make many of these models reachable on a phone, and access arrangements change frequently. Check current terms for the app and model you use, and avoid pasting client or personal data into anything whose retention and training terms you have not read.
Why does the answer change when I ask again?
Sampling is non-deterministic by default. If your app exposes temperature or seed controls, set them. If it does not, treat every run as a fresh sample and verify facts each time.
Build a small prompt kit and reuse it
Save three prompts: the numbered-block template above, the critic pass, and the long-context verifier. Keep one notes file with a single line per model describing what you corrected. Within a month you will have an accurate picture of how each model behaves for your work, and free ai model india prompt threads will become considerably less useful to you. For the persistent-instruction layer, browse the system prompts collection.