Out-of-the-box foundation models are remarkable achievements. They can summarize, translate, and generate content in ways that were impossible just a few years ago. However, an out-of-the-box LLM gives generic responses that sound nothing like your brand. It answers customer queries accurately, but misses your company's tone entirely. Sound familiar? The fix is not retraining—it's better prompting.
For a successful AI system, you need an AI system that truly reflects your organisation's voice, domain knowledge, and operational needs. The work doesn’t end there. The real opportunity begins when you start teaching a model how to think with you. That process starts with prompting. It’s the first and often the most important lever you can pull when optimizing any large language model (LLM).
From commands to conversations
Every interaction with an LLM begins with a prompt. It might seem simple. You type an instruction, and the model responds. But prompting is not about giving commands; it’s about shaping a conversation. The way you frame a prompt determines how the model interprets intent, tone, and context.
This is where the modern large language model reflects something ancient. They demonstrate the timeless value of Socratic questioning—progressing toward understanding by asking, refining, and responding. Each exchange is a dialogue, not a directive. The model’s role is to clarify, probe assumptions, and offer alternatives, much like a thoughtful partner in reasoning.
Thinking of prompts in this way shifts the focus from extraction to exploration; from commands to clarity. It’s how you align the model’s reasoning with your goals. It defines the space in which it can be creative or analytical, concise or exploratory, directive or conversational.
In this sense, prompting is less about getting the model to do something and more about establishing how you and the model will think together.
Why model customization matters
Foundation models are generalists by design. They are trained to handle a wide range of language tasks across various domains. That generalization is a strength, but it also means they lack the nuances of your specific context. Customization fills that gap.
There are several paths to customizing LLMs. Prompt engineering (or prompt-tuning) offers the most accessible starting point. Beyond that, you can move into fine-tuning, retrieval-augmented generation (RAG), or hybrid approaches that combine retrieval and adaptation. Each technique offers different trade-offs between control, cost, and performance.
No matter which approach you use later, prompting underpins them all. It defines how the model behaves at inference time. Getting your prompts right is the foundation of every other optimization step.
Prompting: The first strategic lever
Prompt-tuning is the art of telling the model exactly what you want and how you want it. The system prompt and the user prompt matter the most.
The system prompt defines the model’s role and rules of engagement. It sets expectations for tone, depth, and reasoning style. The user prompt delivers the task or question at hand. Together, they establish the framework for the conversation.
Effective prompting starts with structure. There is no single right way to do it, but successful approaches share a consistent design logic: they make intent explicit and repeatable.
One useful structure frames each interaction around these four elements:
- Context: Supply the background and objectives.
- Role: Assign the model a perspective or domain identity.
- Clarification: Encourage the model to ask questions before acting.
- Task: Assign a specific job to perform.
This is only one of many proven methods. Several other prompt engineering frameworks have emerged across the industry, each emphasizing a different aspect of clarity and control.
Popular frameworks include:
COSTAR – A comprehensive framework for full-stack prompt design.
- Context: Background information the model needs.
- Objective: The goal of the output.
- Style: How the message should be delivered.
- Tone: The voice or attitude of the output.
- Audience: The intended audience for the output.
- Response: The desired structure and format.
CREATE – Often used for creative or marketing applications.
- Context: Relevant background information.
- Role: The persona the AI should adopt.
- Example: A reference for structure and style.
- Audience: The intended reader.
- Tone: The desired voice (e.g., professional or conversational).
- End goal: The final desired result.
CLEAR – A framework focused on prompt clarity and adaptability.
- Concise: Be direct and to the point.
- Logical: Organize information in a sensible order.
- Explicit: Be specific and leave no room for ambiguity.
- Adaptive: Adjust the prompt based on the model’s responses.
- Reflective: Review and refine the prompt to improve outcomes.
While the labels differ, these frameworks share the same intent: to transform prompting from ad hoc experimentation into a disciplined design practice.
These core principles apply across all approaches:
- Iterative refinement: Start with a simple prompt and evolve it based on feedback and results.
- Specificity: Be as precise as possible about the desired task and output.
- Context: Provide essential background, but avoid overloading the model with unnecessary details.
- Reasoning: Break complex problems into smaller, structured steps using techniques, such as chain-of-thought prompting.
The best framework is the one that aligns with your team’s workflow and enables the model to consistently produce relevant, high-quality results. What matters most is not which acronym you choose, but that your prompting process is deliberate, testable, and aligned with your organization’s goals.
This structure transforms prompting from guesswork into design. It turns the model from a reactive tool into a proactive collaborator.
Consider these three common prompt archetypes:
The thought partner:
Ask the model to reason with you, explore trade-offs, and test assumptions.
You are a senior technical consultant with 15 years of experience in distributed systems and cloud architecture. Your role is to help engineering teams make informed decisions by:
Asking clarifying questions about requirements and constraints.
Exploring trade-offs between different approaches.
Identifying potential risks and mitigation strategies.
Challenging assumptions constructively.
Providing structured reasoning for complex technical decisions.
Always ask follow-up questions before giving recommendations.
User prompt example:
Our team is debating whether to adopt Kubernetes for our current monolithic application. We have about 50,000 daily active users and our current infrastructure runs on traditional VMs. Help me think through this decision.
The model becomes a collaborative reasoning partner, not just an answer machine. It will ask about team expertise, budget constraints, timeline, and current pain points before offering guidance.
The explainer:
Ask it to break down complex ideas for a specific audience or skill level. You are a technical writer specializing in making complex concepts accessible. Your job is to:
Break down technical topics into digestible chunks.
Use analogies and examples relevant to the audience's experience level.
Structure explanations from basic concepts to advanced details.
Include practical examples and use cases.
Check for understanding and offer to clarify further.
Always consider your audience's technical background when explaining.
User prompt example:
Explain container orchestration to a team of experienced Java developers new to DevOps. They understand microservices conceptually but haven't worked with containers in production.
The model tailors its explanation to the specific audience, using familiar concepts (Java, microservices) as bridges to new ones (containers, orchestration).
The summarizer:
Provide dense context and ask for key takeaways or simplifications. You are an expert at extracting key insights from technical documentation and meetings. Your role is to:
Identify the most critical information and decisions.
Highlight action items and next steps.
Note any unresolved questions or risks.
Present information in a scannable format.
Maintain technical accuracy whilst improving clarity.
Focus on what busy technical leaders need to know and act upon.
User prompt example:
Here's the transcript from our architecture review meeting [insert meeting notes]. Create a summary for our CTO, covering key decisions made, outstanding concerns, next steps, and any budget/timeline implications.
The model focuses on executive-level concerns whilst maintaining technical depth where needed.
These patterns are reusable, adaptable, and can be refined through iteration. That iteration is where actual performance gains emerge.
Engineering prompts like code
Prompts are not static text. They are living assets that evolve as your application matures. Treat them as part of your codebase. Store them in version control, review changes, and integrate them into your CI/CD workflow.
Prompts should be managed the same way you manage APIs, configurations, or test scripts because they define the behavior of your AI system.
When prompts live in source control, teams can track their evolution, validate their performance, and ensure alignment across environments. This practice turns prompting into a repeatable engineering discipline rather than an experimental art.
Prompt design carries significant technical implications. Every LLM operates within a context window, a fixed amount of text the model can consider at once. Each word in your prompt, along with system instructions and previous messages, consumes part of that space and affects both performance and cost.
Larger context windows enable longer conversations and more reference material, but they also increase computational load. Longer prompts require more GPU memory and can reduce throughput. Conversely, overly short prompts risk losing nuance or omitting vital context that leads to poor results.
For high-throughput applications, reducing average prompt length from 500 to 200 tokens can improve response times by 15-20% whilst maintaining quality similar to optimising a database query. The key is finding the right balance and providing enough information for accurate reasoning without compromising efficiency.
Best practices for performance:
- Start simple, then layer in examples and context only when they add demonstrable value.
- Track both accuracy and latency during experimentation.
- Adjust prompt length, formatting, and retrieval inputs just as you would tune any other system component.
- Remember the most effective prompt delivers the correct result with predictable performance, not necessarily the longest or most detailed one.
This approach turns prompt optimization into an engineering discipline rather than guesswork with measurable impacts on quality and operational costs.
From prompts to memory
Prompting defines how a model starts thinking, but it doesn’t give it memory. Without a memory system, each prompt begins a new conversation with no awareness of what came before.
That’s where retrieval-augmented generation (RAG) and related methods come in. By retrieving relevant information from a knowledge base and injecting it into the prompt, you can ground responses in your organization’s data. This creates the illusion of long-term memory while maintaining full control over what the model can and cannot access.
Prompting and retrieval complement each other. Prompting defines how the model should reason; retrieval defines what it should know. When combined, they deliver accuracy, consistency, and domain alignment.
Building AI the open way
Platforms such as Red Hat OpenShift AI make this process easier by providing a consistent inference environment powered by an open, scalable architecture. This allows teams to iterate on prompts, evaluate outputs, and deploy updates without retraining or redeploying models.
By managing prompts, context, and data pipelines as part of a continuous development process, organizations can improve model behavior predictably and transparently. This approach reflects the broader open source philosophy: modular, auditable, and built for collaboration.
The real work begins with prompting
Optimizing an LLM-based AI system doesn’t start with retraining models or adding retrieval layers—it starts with the words you use to guide it. Prompting is the foundation of effective AI customization and the fastest path to measurable improvement.
Every organization experimenting with generative AI will eventually discover the same truth: prompts are not just inputs; they are design decisions. They shape how intelligence emerges from data. When treated with the same care as code, they turn a general-purpose model into a system that truly understands your context, goals, and your way of thinking.
Start here with a Red Hat OpenShift AI product trial.
