Cohere's Command models will do exactly what you tell them to do, which is either the best news you've had all week or the worst, depending on how good your instructions are. Ask for a summary without specifying length, tone or focus and you'll get three different answers to the same prompt on three different runs. That isn't a quirk in the model. It's what happens when you leave the model to guess.
We build AI products and data foundations for clients at Shipshape Data, and prompt design is one of the first places we watch a project either pick up speed or stall. A prompt that's loose in the wrong place turns into hallucinated facts, formatting that shifts from run to run, and outputs nobody trusts enough to ship without a human checking every line. Fix the prompt and half of that disappears overnight.
This is a practical guide to prompting Cohere's Command family well: how the system prompt and user turn actually work, which techniques reliably improve output quality, two templates you can lift straight into a project, and how to test a prompt instead of just hoping it holds. None of it is exotic. Most of it is discipline that teams skip because it feels slow, right up until the point it would have saved them a week.
Why Cohere prompting isn't GPT prompting with different words
Command models are instruction-following models Cohere built with enterprise use cases in mind: classification, extraction, retrieval-augmented generation, internal tooling. That design choice shows up directly in how the models respond to prompts. Teams that carry over habits from GPT-style conversational prompting often get results that are technically fine but not quite what they wanted, and they can't immediately say why.
The short version: Command rewards specificity in a way conversational models sometimes let you get away without. Ask it to "summarise this document" and you'll get something usable back, but the length, tone and focus drift between runs. Tell it the target length, the audience and what to leave out, and the same prompt produces the same shape of answer every time. That gap between vague and specific is wider with Command than with models trained more heavily on open conversation, and it's the single biggest source of frustration we see from teams new to the platform.
Specificity is not a nice-to-have here
This matters more than it sounds like it should. A prompt vague enough to produce three different answers on three different runs is a prompt that can't sit inside a production pipeline. Somebody has to review the output every time, which defeats the point of automating the task in the first place. Write the instruction so there's only one reasonable way to follow it, and the review step gets much shorter.
There's a second habit worth building early: place your context correctly. Cohere's API separates a system prompt from the user turn, and where you put things changes how consistently the model behaves. Stuff everything into the user turn, persona, rules, task and data all at once, and the model has to re-derive your intent every single call. Split it properly and the model's behaviour holds steady across a conversation or a batch of a thousand requests. Get this right on day one. It's much cheaper than fixing it after a client has already seen the inconsistency.
The two layers Cohere gives you: system prompt and user turn
Cohere's API structures every call around two distinct inputs, and understanding what belongs in each one is close to the whole game. The system prompt sets persistent behaviour: persona, tone, output format, hard constraints. The user turn carries the actual request: the task, any retrieved context, and whatever input data the model needs to do the work.
What belongs in the system prompt
Use the system prompt for anything that should hold true across every call, not just this one. If your application always needs bullet-point answers, that rule lives here. If the assistant should refuse questions outside a specific domain, that boundary lives here too. Think of it as the standing instruction set the model reads before the user has said a word.
Keep it tight. A system prompt loaded with contradictory or half-finished instructions produces exactly the inconsistency you're trying to engineer away. We've reviewed system prompts running to twenty rules, several of which quietly contradicted each other, and the fix wasn't adding a twenty-first rule to patch the conflict. It was cutting the list to the handful of rules that actually mattered and dropping the rest.
Structuring the user turn
The user turn is where the task and the data live, and separating the two clearly makes a real difference to output quality. Label your sections: "Instruction:" for what you want done, "Context:" for what the model should work with. It looks almost too simple to matter, but a labelled prompt gives the model far less room to guess which part is the job and which part is the material.
Order matters too. Put the instruction before the data. Command models tend to follow the task more reliably when they know what they're looking for before they read a long block of context, rather than working out the goal only after wading through several paragraphs of retrieved text.
The techniques that actually move the needle
A handful of techniques account for most of the improvement we see when a prompt goes from unreliable to production-ready. None of them are Cohere-specific tricks exactly, but Command responds to them with unusual consistency.
Few-shot prompting
Few-shot prompting means showing the model two or three worked examples of the input-output pattern you want, rather than describing the pattern in the abstract. Command models are particularly good at inferring constraints from examples that would take several paragraphs to spell out as rules. Show it, don't just tell it.
Choose your examples for coverage of the awkward cases, not the easy ones. A single example that handles ambiguous input teaches the model more than three examples of the same straightforward case repeated with different words. If your classification task has an edge case that keeps tripping up reviewers, put that edge case in the prompt as an example, not just in your documentation.
Output format control
Say exactly what shape you want the answer to take, and leave nothing for the model to decide on your behalf. Formats worth specifying explicitly include:
- JSON with named fields, so downstream code can parse it without guessing at keys
- A numbered or bulleted list when the answer has discrete parts
- A fixed word count or paragraph length when the output feeds a UI with limited space
- Plain prose with a stated maximum length, for anything that needs to read naturally rather than look structured
This is the cheapest fix available to most broken prompts. Teams spend hours tuning wording and hardly any time specifying format, and format is usually where the inconsistency actually lives.
Chain of thought for reasoning tasks
For anything involving multi-step logic, comparison or arithmetic, add a short instruction asking the model to work through its reasoning before giving a final answer. "Think through this step by step before giving your final answer" is close to the whole trick. It reduces the confident-but-wrong answers that reasoning tasks are prone to, and it gives you a visible trail to check when an output looks off.
One caveat: chain of thought adds tokens and latency, and for simple lookups it's overkill. Reach for it when the task genuinely involves reasoning, not as a default you bolt onto every prompt out of habit.
Picking a model and setting the parameters that shape output
Cohere ships several Command variants, and prompt engineering doesn't happen in isolation from that choice. Command R and Command R+ are built for retrieval-heavy and tool-use workloads, with longer context windows than the smaller models, and they tend to follow structured prompts more faithfully. If your task leans on retrieved documents or calls out to external tools mid-conversation, start there rather than reaching for a smaller model and fighting it with elaborate prompt engineering. A well-chosen model does part of the work a clever prompt would otherwise have to do on its own.
Temperature and the shape of your task
Temperature controls how much the model varies from its most likely next word. For extraction, classification and anything with one correct answer, keep temperature low, close to zero, so the same input produces the same output run after run. For brainstorming, drafting marketing copy or generating variations, a higher temperature gives you the diversity you're actually after. Teams sometimes leave the default in place regardless of task, then wonder why a classification pipeline that's meant to be deterministic keeps returning slightly different labels for the same input on different days.
Max tokens matters more than people expect, too. Set it too low and the model truncates mid-sentence, sometimes mid-JSON-object, which breaks a parser expecting a complete structure. Set it too generously and you pay for verbosity you didn't ask for, because the model will happily fill the space you gave it. Size the limit to the output you specified in your prompt, not to whatever the API's default happens to be.
Stop sequences for cleaner cuts
If your prompt template has a clear boundary, such as the "Output:" line in the classification template below, a stop sequence tells the model exactly where to end generation instead of relying on it to stop of its own accord. This is a small detail that saves you a regex cleanup step on every response, and it closes off a category of bug where the model quietly starts a second, unwanted example straight after the first.
Two templates worth stealing
Reusable templates keep your prompting consistent across a project instead of reinventing structure every time someone writes a new one. Treat both of these as starting points you adapt to your data, not finished prompts you paste in unchanged.
RAG answers that stay grounded
Retrieval-augmented generation is one of the most common uses of Cohere in enterprise settings, and the system prompt decides whether the model stays grounded in the retrieved documents or starts filling gaps from its own training. Get this wrong and the model answers confidently from memory instead of admitting it doesn't have the information.
The clearer you are about what the model must not do, the less room it has to fabricate an answer your users can't check.
A template that holds up well in practice:
System: You are a knowledge assistant. Answer the user's question using only the context provided below. If the context does not contain enough information to answer, say "I don't have enough information to answer that."
Context: [insert retrieved documents here]
User: [insert user question here]
Label every section. It costs you nothing and it removes an entire category of ambiguity about what the model is meant to do with each block of text.
Classification and extraction
For labelling text or pulling structured data out of unstructured input, pair a few-shot format with an explicit output constraint. Two examples are usually enough to lock the output shape in without burning tokens on a longer list:
System: You are a data extraction assistant. Return your answer as valid JSON only. Do not include any explanation.
Input: "Delivery was late by three days." Output: {"category": "logistics", "sentiment": "negative"}
Input: "The product quality exceeded our expectations." Output: {"category": "product", "sentiment": "positive"}
The "no explanation" line matters more than it looks. Without it, Command will sometimes wrap the JSON in a sentence of preamble, and now your parser has to handle two output formats instead of one.
Treat prompts like code: testing and version control
A prompt that performs well on ten hand-picked examples can degrade badly the moment it meets real edge cases at volume. The only way to catch that before your users do is to test prompts the way you'd test code, before deployment and on an ongoing basis afterward.
Track every change
Version your prompts the same way you'd version anything else that ships to production. Every change gets a note: what changed, why, what you expected to happen. Skip this and you lose the ability to work out which change caused a regression, so the fix becomes reverting everything and starting again instead of undoing the one line that broke it.
A shared document with a running changelog is enough to start. You don't need tooling for this. You need the habit.
Evaluate against criteria, not vibes
Before you run a single test, write down what a good output actually looks like. For a summarisation task that might mean: under 100 words, neutral tone, covers three named points from the source. Vague criteria produce vague evaluation, and you end up judging outputs by feel, which means two people on the same team can disagree about whether a prompt works.
Build a small test set that includes the easy cases, the awkward ones, and anything that's caused a real problem before. Run the prompt against that set every time you change it. It takes ten minutes and it tells you, reliably, whether your change helped or made things worse.
Where teams go wrong
The mistakes repeat across almost every client engagement we've worked on, so they're worth naming plainly.
The first is treating the first working version as the finished version. A prompt that produces a good answer once hasn't been tested, it's been tried. The second is skipping the system prompt entirely and cramming persona, rules and task into a single user message, which works fine until the conversation runs past a couple of turns and the model starts losing track of the standing instructions. The third is writing prompts nobody owns. Six months in, nobody remembers why a particular line is there, so nobody will touch it, and the prompt calcifies into something everyone's afraid to change.
None of these are difficult to avoid. They're just easy to skip when a deadline is close and the first draft technically works.
Start with the fundamentals
Specify your output format. Use few-shot examples for anything format-sensitive. Version every change you make to a prompt that's live. Most of the production issues we see trace back to one of those three being skipped, and fixing them removes most of the friction teams hit moving a Cohere feature from a demo into something that survives contact with real users.
If you're working through this on a live project, whether that's a RAG pipeline, a classification system or a customer-facing assistant, and want a second pair of eyes on the prompt design or the pipeline around it, talk to us. We'd rather help you catch the gaps before they reach production than after.