Cohere prompt engineering: techniques, tips, and examples

Cohere's Command models adhere to instructions precisely. This behaviour is beneficial when your instructions are clear, but it creates problems when they are vague. If you request a summary without specifying the length, tone, or focus, the model will generate different results each time the prompt is run. This happens because the model must make its own assumptions when specific details are missing.

Cohere prompt engineering involves structuring prompts for Cohere's Command models so that outputs remain consistent across multiple runs. This includes keeping the system prompt separate from the user input, using a few examples to show the desired format for tasks where formatting matters, and clearly defining the expected output structure. When done correctly, this process can turn a model that initially performs inconsistently into one suitable for production use.

Shipshape Data builds AI products and data foundations for its clients. Prompt design is often the point where projects either gain momentum or encounter delays. When a prompt is imprecise, it results in factual errors, inconsistent formatting, and unreliable outputs that require constant human review. Improving the prompt resolves many of these issues right away.

This guide explains how to prompt Cohere's Command models effectively. It covers how the system prompt and user input work, techniques to improve output consistently, two templates you can use immediately, and a method for testing prompts to ensure performance stays consistent. These are standard practices, but teams often avoid them because they seem time-consuming, even though they can save significant time in the long run, potentially a week or more.

Why Cohere prompting isn't GPT prompting with different words

Command models are instruction-following models that Cohere built with enterprise use cases in mind: classification, extraction, retrieval-augmented generation, internal tooling. That design choice appears 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 exactly what they wanted, and they cannot immediately explain why.

Command requires more specific instructions than conversational models do. If you ask it to "summarise this document", the model provides a usable result, but the length, tone, and focus will vary between attempts. If you specify the target length, the audience, and what information to exclude, the prompt generates a consistent answer every time. The difference between vague and specific prompts is more pronounced with Command than with models trained for open conversation. This difference is the primary cause of frustration for teams that are new to the platform.

You must be specific in this case

This detail is actually more significant than it appears. If a prompt is vague enough to generate three different results across three attempts, it is not suitable for a production pipeline. A person would need to review the output every time, which eliminates the benefits of automation. You should write instructions that allow only one reasonable interpretation. This makes the review process much faster.

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 place 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 on every single call. Split it properly and the model's behaviour stays steady across a conversation or a batch of a thousand requests. Get this right on day one. It is 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 essentially 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 instructions that apply to every interaction. If your application requires responses in bullet points, place that rule in the system prompt. If the assistant must decline questions about specific topics, define those limits here. This prompt serves as the permanent set of instructions the model reviews before receiving any user input.

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 contains both the task and the data. Separating these two elements improves the quality of the output. Use labels for your sections, such as "Instruction:" for the task and "Context:" for the source material. This method is simple, but it ensures the model does not have to guess which part of the prompt is the command and which part is the data.

The order of your prompt is important. You should place the instruction before the data. Large language models follow tasks more reliably when they understand the goal before reading a long block of context. This is more effective than requiring the model to determine the objective only after it has read several paragraphs of retrieved text.

The techniques that actually move the needle

A few specific techniques provide most of the improvements needed to make a prompt reliable for production. These methods are not unique to Cohere, but the Command model responds to them with high consistency.

Few-shot prompting

Few-shot prompting involves providing the model with two or three examples of the desired input-output pattern instead of describing the pattern abstractly. Command models are effective at identifying constraints from these examples that would otherwise require several paragraphs of written rules. You should provide specific examples rather than relying only on instructions.

Select examples that address difficult scenarios rather than simple ones. One example demonstrating how to handle ambiguous input provides more instruction to the model than three examples of basic cases using different phrasing. If a specific edge case in your classification task frequently confuses reviewers, include that case as an example within the prompt instead of limiting it to your documentation.

Output format control

State the exact format you want the answer to take. Do not leave any decisions to the model. You should explicitly specify formats such as:

• 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
• Use a fixed word count or paragraph length when the output goes into a user interface with limited space.
• Use plain prose with a specific maximum length for any content that must read naturally and avoid a structured appearance.

This is the cheapest method to correct most ineffective prompts. Teams frequently devote a long period to tweaking the wording, yet they often overlook specifying the format. The format usually causes inconsistent results.

Chain of thought for reasoning tasks

When you use prompts for multi-step logic, comparisons, or arithmetic, include a short instruction for the model to show its reasoning before it provides a final answer. The phrase "Think through this step by step before giving your final answer" is the primary method for this. This practice reduces the frequency of incorrect but confident answers common in reasoning tasks. It also provides a visible record you can check if the output seems incorrect.

There is one limitation. Chain of thought increases token usage and latency; it is unnecessary for simple lookups. You should use it when a task requires actual reasoning rather than applying it to every prompt by habit.

Picking a model and setting the parameters that shape output

Cohere offers multiple Command model variants. You should select a model based on your specific requirements because prompt engineering depends on the model you use. Command R and Command R+ are designed for tasks involving heavy retrieval and tool use. These models have larger context windows and follow structured prompts more accurately than smaller models. You should use these variants if your project requires retrieving documents or using external tools during a conversation. Choosing a smaller model and attempting to compensate with complex prompt engineering is often inefficient. Selecting the correct model reduces the amount of effort required to write effective prompts.

Temperature and the structure of your task

Temperature settings control the degree to which the model strays from the most likely next word. When carrying out extraction, classification, or any task that has one correct answer, you ought to keep the temperature close to zero. This guarantees that identical input consistently yields identical output. For brainstorming, drafting marketing copy, or generating variations, a higher temperature yields more diverse results. Teams frequently apply the default setting to all tasks. This can cause issues where a classification pipeline meant to be deterministic returns different labels for the same input over time.

The maximum token limit matters more than many users realise. If you set the limit too low, the model will truncate in the middle of a sentence or a JSON object. This creates errors in parsers that need a complete data structure. If you set the limit too high, the model may generate unnecessary text that raises your costs, because it tends to fill the available space. You should set the limit based on the specific output length you asked for in your prompt rather than relying on the default API setting.

Use stop sequences to obtain cleaner outputs

If your prompt template contains a distinct boundary, such as the "Output:" line in the classification template below, a stop sequence will tell the model to stop generation at that exact point. This stops the model from ending the output at its own discretion. Applying this setting removes the need to clean each response with a regular expression. It also avoids a particular error where the model produces a second, unwanted example right after the first one.

Two templates you can use

Reusable templates keep your prompting consistent across a project. This stops team members from building new structures for each new prompt. You should view these examples as starting points to tailor to your specific data instead of as final prompts to be used unchanged.

RAG answers that stay grounded

Retrieval-augmented generation is a common use of Cohere in business settings. The system prompt decides if the model draws on the information in the retrieved documents or uses its own knowledge to finish the response. If the prompt is not crafted well, the model might answer confidently with its internal data rather than indicating that it lacks the required information.

If you give clear instructions on what the model must not do, it will be less prone to produce incorrect information that users cannot verify.

This template works well in practice:

System: You act as a knowledge assistant. Respond to the user's question using only the context given below. If the context lacks sufficient information to answer, state "I don't have enough information to answer that."

Context: [insert retrieved documents here]

User: [insert user question here]

Label each section. This approach costs nothing and removes uncertainty about how the model should handle each text block.

Classification and extraction

When labelling text or extracting structured data from unstructured input, apply a few-shot format together with an explicit output constraint. Usually, two examples are enough to define the output structure without adding unnecessary tokens to a longer list.

System: You function as a data extraction assistant. Give your answer only in valid JSON format. Do not include an 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" instruction matters. If you omit it, Command might insert a sentence before the JSON. This causes your parser to handle two different output formats rather than one.

Treat prompts like code: testing and version control

A prompt that succeeds on ten chosen examples can break when it meets numerous varied edge cases. You need to test prompts the same way you test code to spot these problems before users encounter them. This testing ought to be performed before deployment and repeatedly during production.

Track every change

Version your prompts with the same techniques you apply to other production code. Log each change, noting what was altered, why it was updated, and the anticipated outcome. If you skip this process, you cannot pinpoint which particular change caused a regression. This compels you to roll back all recent changes and begin again instead of just correcting the single line that introduced the error.

Start by using a shared document that contains a running changelog. Specific software is not required for this process. You only need to develop the habit.

Evaluate against criteria, not vibes

Set the requirements for a successful output before starting testing. In a summarisation task, the criteria could be a 100-word limit, a neutral tone, and the inclusion of three specific points from the source text. Unclear standards cause inconsistent evaluations. Without clear metrics, you will assess results using personal intuition, enabling different team members to disagree on a prompt's effectiveness.

Build a small test set that includes simple cases, unusual examples, and any previously encountered issues. Execute the prompt on this set each time you modify it. This process requires ten minutes. It offers a reliable method to see whether a change improved the results or introduced problems.

Common mistakes that teams make

We have seen the same mistakes in almost every client project we have completed. These errors are listed below.

The first mistake is treating a working version as a final product. If a prompt yields a good result once, it has only been tried, not tested. The second mistake is omitting the system prompt and placing the persona, rules, and task together in a single user message. This approach works at first, but the model frequently loses track of the instructions after several conversational turns. The third mistake is creating prompts that lack ownership. After six months, no one recalls the purpose of specific lines. Consequently, no one modifies the text, and the prompt turns into a permanent structure that people fear to change.

These issues are easy to avoid. However, people often miss them when a deadline looms and the initial draft works correctly.

Start with the basic principles

Define the format of your output. Provide few-shot examples for any tasks that require a specific format. Keep a version history for every change made to a live prompt. Most production problems result from skipping one of these three steps. Following these practices eliminates most of the difficulties teams face when moving a Cohere feature from a demonstration into a tool for actual users.

If you are currently developing a live project, such as a RAG pipeline, a classification system, or a customer-facing assistant, you can talk to us for a review of your prompt design or pipeline architecture. We prefer to help you identify potential issues before your system reaches production rather than after it has launched.

Thinking about building this for your business? Our AI developers will tell you what it depends on, including whether your data is ready to hold it up.

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