Prompt Engineering as Part of the Development Process: Key Techniques Explained

Intro: The Shift in Modern Software Development

Not long ago, the boundaries of software development were clearly defined. Engineers wrote code, systems followed deterministic logic, and “intelligence” in software was limited to what developers explicitly programmed. But that model is now changing.

Today, large language models (LLMs) and generative AI are transforming how we build, test, and even think about software. AI-powered applications don’t just execute predefined rules – they analyze data, reason, and adapt their actions to the context and environment. So, instead of, or rather in addition to, creating “traditional”, deterministic rules for software systems, developers also come to control how these systems reason, interpret instructions, and make decisions in a non-deterministic way. Such a shift in programming is giving rise to prompt engineering as a discipline and competency, not just a way to communicate with LLMs.

At the same time, the presence of generative AI in application logic is game-changing for businesses. It turns software from static to adaptive by design. As a result, applications can dynamically respond to new data, evolving user needs, and changing business contexts – all without requiring traditional reprogramming. Systems that once needed months of tuning can now learn and refine their behavior in near real time, through improved prompts and contextual logic.

So, as AI continues to appear in every layer of the technology stack, prompt engineering comes to play as the connective tissue that ensures systems don’t just run efficiently, but behave intelligently. Thus, in this article, we explain how prompt engineering works in AI software development, how we use different prompt engineering techniques in practice, and what skills define a good prompt engineer.

What is Prompt Engineering?

As you know, interaction with large language models (LLMs) consists of two essential parts: input and output. You input a question or instructions – a prompt, and the LLM outputs the answer – a response. So, prompt engineering is a way to manipulate the input text in a way to coax the model into generating relevant, accurate, and context-aware responses, tailored to our task or business focus. But how is it made possible?

At its core, prompt engineering leverages the pattern recognition and contextual understanding capabilities of LLMs. During pretraining, a model is exposed to hundreds of billions of tokens (units of text). Each token is converted into a numerical vector representation in a high-dimensional space, which we call an embedding. These embeddings capture not just the token itself, but its relationships and meaning relative to the surrounding tokens. This way, the model builds a map of how words and phrases of natural language relate to each other across countless contexts.

Once these embeddings are in place, the model learns to predict the most probable next token given the previous sequence. It analyzes patterns across all training data: which words commonly follow which, how syntax and grammar flow, and how meaning is constructed over longer contexts. Through repeated exposure to these patterns, the model internalizes statistical relationships and this allows it to generate coherent, contextually appropriate text, even in new situations.

Thus, large language models don’t “understand” natural language in a symbolic or rule-based sense, like humans do. Instead, they are trained to predict the next word or phrase given the preceding context. As a simple example, the phrase “the capital of France is” produces embeddings that bias the probability distribution toward “Paris”, because the model recognizes that this completion has appeared the most frequently in its training dataset.

This mechanism is what makes prompt engineering a powerful tool for using large language models to our benefit: a model’s token predictions depend entirely on the input it receives. So, a prompt acts like a lens or a map, shaping the model’s internal representations and guiding its reasoning process. By carefully crafting prompts, we can ensure the model completes a task we need, maintains factual accuracy, follows desired style and tone, and produces outputs that meet specific requirements.

For example, good prompts will define:

• Roles and constraints (e.g., “Act as a compliance auditor for financial reports.”)
• Data references (e.g., “Use only information from the provided dataset.”)
• Output format (e.g., “Return the summary as JSON with specified fields.”)
• Evaluation standards (e.g., “If confidence is low, ask for clarification before generating a result.”)

Each prompt component affects the AI’s internal decision-making processes. Poorly designed prompts lead to inconsistent, inaccurate, or verbose outputs.

What is the Role of Prompts in Software Development?

Most modern software users have practiced some form of prompt engineering when using ChatGPT, Perplexity, or any other LLM-based app with a chat interface. Many professionals have become quite advanced in prompting (so-called power-users), so they can automate entire business workflows, create amazing content, or drastically speed up lengthy processes with a single or just several prompts.

But in software development, prompt engineering is even more than that. We don’t just use prompts and LLM-powered tools to solve coding challenges or streamline operations. In addition to writing code that explicitly defines how an app should run, we create system prompts — hidden, well-structured instructions that shape how an LLM, as a part of app logic, interprets the problem, reasons about it, and delivers output to the end user.

Here, our goal is to make sure the model operates within the correct context, tone, data boundaries, and logic to provide a desired output, but with much more complex queries. This requires understanding both the business objective and the technical behavior of large language models — biases, token limits, response structure, memory, context windows, etc.

How Prompts Work Beyond Code Generation

As AI integrates deeper into software products, prompts have come to act as dynamic logic components embedded directly into application workflows. Thus, in addition to defining behavior through traditional code, teams now can use natural-language prompts to shape how the system reasons, interprets input, makes decisions, and enforces business rules. Moreover, prompts become a flexible, soft-coded layer of application logic that can adapt much faster than manually written code. 

In this chapter, we explain the ways prompts serve as operational logic inside modern software applications.

Prompts as Business Rules Engines

In software products, certain system logic is driven by business rules: how to prioritize incoming requests, when to escalate, which tone to use, or what compliance steps to follow, and so on. In the pre-AI era, developers implemented this logic within the application using conditional structures. This often resulted in rule evaluation being dispersed throughout the codebase, making it harder to manage and evolve over time.

Meanwhile, GenAI and prompts enable programmers to express these rules more concisely in natural language. The model interprets them relative to every interaction and adjusts the system’s behavior dynamically, with no need to provide separate instructions to every possible condition. This is particularly valuable for situations when the system logic depends on understanding the context, interpreting user sentiment, or resolving ambiguity.

Prompts as Workflow Controllers

Prompts can also act as procedural logic and define the exact steps an underlying LLM should take before producing a result. This allows us to replace explicit and complex control structures expressed in code with a step-by-step reasoning chain described in natural language.

This works perfectly for tasks that require reasoning. For instance, to program a meeting assistant that can extract discussed topics, action items, and agreed decisions in a consistent, structured format, even though the source conversation may be chaotic.

Prompts as Validators and Guardrails

Prompts can also be used to enforce constraints on input data or user actions, much like validation logic does in the back-end code. Using prompts, we can instruct the model what it must not do, what it must double-check, and what rules it must follow. 

Moreover, with traditional code, we can validate a limited set of data characteristics, such as data types or data formats. Meanwhile, prompts and LLMs can check meaning, context, and factual grounding — areas where a purely algorithmic approach won’t operate.

Prompts as Data Transformers and Extractors

Many applications need to transform unstructured input into structured output to function properly. Code can parse strict formats, but it struggles when natural language variability comes into play. Meanwhile, prompts convert LLMs into extremely flexible data transformation layers.

For instance, we worked on a billing automation tool for a legal organization that had to extract relevant data from lengthy regulatory documents and apply it to invoice validation. This task would require hundreds of lines of code to express parsing logic. Instead, we used several prompts to define data transformation logic that would adapt to countless guideline phrasing variations.

Prompts as Dynamic Configuration

Prompts are easy to understand and edit, even for non-technical application admins. So, having prompts at their fingertips, product owners can effortlessly adjust tone, policy, persona, or allowed actions without waiting for a deployment cycle.

In this aspect, prompts are similar to dynamic configurations, like feature flags, content management systems, policy files, or role configurations: changes made to them take effect instantly at runtime.

Powering Agent Behavior and Action Logic

In agentic applications, prompts help define how the AI chooses actions, which tools to use, and when to use them. They replace manually coded decision trees that would be brittle or impossible to write at scale. Thus, we use prompting as a decision-making framework for autonomous or semi-autonomous systems.

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To achieve optimal results with prompt engineering for each use case, we leverage different prompting techniques. Each technique focuses on specific prompting patterns that help us achieve various degrees of output accuracy, ways of response structuring, or control levels over LLM reasoning.

Prompt Engineering Techniques

Prompt engineering encompasses a variety of techniques tailored to different use cases and complexity levels. In this chapter, we review these approaches, from more straightforward ones to more complex, to demonstrate how prompt engineering has evolved over time and, with professional assistance, it can be used in different software applications. 

1. General Prompting/Zero-Shot

Zero-shot prompting is probably the most widely used method both for developers and the general public. It involves providing the model with a plain task description without any examples. In this case, the model relies solely on its pre-trained knowledge to generate responses. For instance, asking an LLM, “Summarize the following article,” without prior examples is zero-shot prompting.

No dataset or example preparation required makes this approach really convenient, fast, and simple. However, while it works well for widely known facts and well-defined common tasks, with complex or domain-specific tasks, the model's performance will likely be inconsistent. This also brings on a higher likelihood of hallucinations with insufficient context.

Considering these aspects, the best use cases for this prompting technique will include:

• Quick ad-hoc queries where examples are unnecessary.
• Simple tasks or when the model is well-aligned with the task.
• Exploratory testing of a model’s capabilities.

2. One-Shot, Few-Shot Prompting

One-shot and few-shot prompting improve upon zero-shot by including one or a few examples within the prompt to demonstrate the desired output format or style. For example, when asking the model to generate a reply for a new email, our prompt will include a sample customer email and an appropriate response. 

This technique significantly improves accuracy for complex or structured tasks as compared to zero-shot by instructing the model to mimic the style, format, or reasoning approach shown in examples. On the flip side, the model may overfit to the examples, producing less flexible outputs. This is why it is important to select or craft representative and inclusive examples that won’t degrade the model’s performance. But keep in mind that adding lengthy or numerous examples will increase token usage and cost.

This technique is useful for tasks with nuanced requirements or when zero-shot is not effective:

• Tasks with specific output formats, domain-specific language, or structured responses.
• When zero-shot results are insufficient, but full fine-tuning will be an overkill.

3. System, Contextual, and Role Prompting

This prompting approach relies on setting explicit instructions or roles within the prompt to shape the model’s behavior. This way, we shape the LLM’s output by controlling what it should do (instructions), what information it can use (context), and how it should behave or speak (role/persona):

• System prompts set high-level rules or constraints for the model, instructions about what the model can and cannot do, such as “You are a financial analyst summarizing reports. Always answer concisely. Never speculate on unknown data.” 
• Contextual prompts provide background knowledge, relevant data, or situational info, for example, a specific policy document, a dataset snippet, or previous conversation turns: “Include the quarterly earnings data in the prompt.”‍
• Role prompts assign a persona or expertise to the model to influence the output style, model behavior, or perspective, like “Use a professional, concise tone for executives.”

Such types of prompts help create consistency across all model’s outputs, which is especially useful for multi-step or long-form generation. It also helps set up specialized interactions through human-like role adoption. At the same time, it is important to keep the balance and avoid overly rigid roles that can limit creativity or lead to conflicting instructions. This will lead to ambiguity in context and produce errors.

The best use cases for this prompting technique will include:

• Professional or compliance-critical outputs.
• Multi-turn conversations that require consistent model behavior.
• Tasks that need a specific tone, style, or domain knowledge.

4. Chain-of-Thought (CoT) Prompting

Chain-of-thought prompting encourages the model to reason through problems step-by-step before arriving at an answer. By prompting the model to “think aloud,” it can handle complex reasoning tasks more effectively. For example, in financial forecasting or troubleshooting scenarios, CoT prompts guide the model to break down the problem into logical steps, thus improving reasoning accuracy and transparency.

This technique greatly improves accuracy on reasoning-heavy tasks and helps reduce hallucinations in structured problem-solving contexts. Also, CoT makes model behavior more interpretable and manageable to users and prompt engineers because they can see the reasoning steps. For example, we leveraged the strengths of this prompt engineering technique to help our client automate their billing process with LLMs.

But benefits come at a price: careful prompt design with thought-through examples is a must if you wish to avoid confusing the model. Also, longer outputs will increase token usage and latency.

Thus, CoT will be an optimal choice for such use cases as:

• Tasks requiring multi-step reasoning, logical deduction, or numerical calculations.
• Solving math problems, planning tasks, or complex decision-making scenarios.

5. Tree-of-Thoughts Prompting

Tree-of-Thoughts extends Chain-of-Thought (CoT) prompting by allowing the model to branch its reasoning into multiple possible paths instead of following one linear chain. The LLM explores alternative reasoning “branches,” evaluates partial solutions, and selects the most promising path (often with external control logic or a search algorithm guiding it).

This technique helps to encourage exploration and avoid premature conclusions. As a result, the model produces higher-quality reasoning and more accurate final answers. Like CoT, it also makes the model’s reasoning process interpretable and auditable.

However, ToT has certain application limits: it requires orchestration logic or external evaluation to select the best branch, which makes it harder to implement in standard chat interfaces without automation tools. Also, multiple reasoning branches make the method computationally expensive.

With tree-of-thoughts prompting, the model mimics human brainstorming and analytical processes, offering richer insights and making it good for complex use-cases:

• Complex reasoning or problem-solving tasks with multiple solution paths (e.g., strategy planning, math puzzles, or business scenario analysis). 
• Decision-making problems where intermediate reasoning can be scored and compared.

6. Step-Back Prompting

Step-back prompting explicitly instructs the model to pause and generalize before answering a question. Instead of diving into the task directly, the LLM first describes the broader context or higher-level concept, then uses that overview to produce a more accurate or grounded answer.  

This technique is simple in application and works great as a self-correction mechanism: by abstracting, it helps the model reduce errors and hallucinations, and by maintaining logical consistency and relevance, it improves overall response quality. 

However, step-back prompting cannot be a remedy in cases when factuality issues arise from the lack of the model’s domain knowledge. Also, if not well-balanced, the model’s responses with this technique risk getting over-generalized.

Effective applications of step-back prompting include:

• Abstract reasoning or conceptual tasks.
• Situations where the model tends to overfit on surface details or lose context (e.g., interpreting ambiguous user questions, summarizing large contexts, or drawing conclusions from long texts).
• In customer service or content generation, step-back prompting ensures that the model’s answers are coherent and aligned with business policies, reducing the risk of misinformation.

7. ReAct (Reason&Act) Prompting

ReAct prompting integrates reasoning traces (the model’s thought process) with actions (e.g., retrieving information, using tools, or making API calls). The model alternates between thinking (“Let me reason this through...”) and acting (“Search for X”, “Retrieve Y”), often within an orchestrated agent framework.

Such an approach improves factual grounding by combining reasoning with verifiable data and makes the model’s thought process transparent and debuggable. On the other hand, ReAct requires complex orchestration ( involving tool integration and parsing model outputs) and has a higher risk of compounding reasoning errors if not controlled.

This hybrid approach enables more interactive and dynamic applications, such as:

• Virtual assistants that can retrieve real-time data or automate workflows based on user queries.
• Autonomous agents and dynamic workflows.
• Interactive decision-making or information retrieval tasks.

8. Automatic Prompt Engineering

Automatic Prompt Engineering uses another model or optimization algorithm to generate, test, and refine prompts automatically. It iteratively searches for prompts that yield the best performance for a given task, often through scoring or reinforcement learning.

This technique accelerates deployment and reduces the manual experimentation burden on teams, making prompt engineering scalable for businesses with diverse AI applications. Automatic prompt engineering helps discover unconventional yet high-performing prompts. And enables systematic prompt optimization for consistent performance at the same time.

As this method is quite complex, it requires infrastructure for automated testing and evaluation, and is highly dependent on the quality of the evaluation metric. Also, as opposed to ToC or ToC, it has limited interpretability as optimized prompts may be non-intuitive or opaque.

The best use cases of this technique include:

• Scaling prompt design across many tasks or datasets.
• Finding high-performing prompts when human trial-and-error would be too costly.
• Enterprise settings where multiple teams need optimized task-specific prompts.

This multitude of approaches makes prompt engineering a powerful software development tool, but a complex skill at the same time. It requires deep understanding of software systems, data structures, and LLM logic — plus the linguistic precision to clearly communicate instructions to the machine. In the next chapter, we break down the prompt engineering competency into key components.

Prompt Engineering as a Key Modern Developer Competency

Skilled prompt engineers have to think more like system architects: they should be able to model how the AI will function as one element within an interconnected system.

As a software engineering competency, prompt engineering combines the expertise of software design with the skill of human communication. It also requires a deep understanding of how LLMs interpret information, reason about context, and generate responses. And, just like coding, it also relies on experimentation, debugging, testing, and continuous optimization.

Structured Thinking 

First of all, prompt engineering requires the ability to translate business needs into structured, clear, and complete instructions that an LLM can follow reliably. This includes a mix of competencies:

• Analytical skills: Ability to deconstruct a business problem into smaller reasoning tasks, each aligned with a model’s capabilities.
• Verbal competence: Ability to phrase instructions unambiguously, break down complex requests into step-by-step instructions, and craft representative examples that the model can follow.
• Context awareness: Understanding of the roles, perspectives, and contextual constraints (audience, format, compliance rules, etc.) that should guide the style and depth of the output and embedding them directly into prompts. Role and rule definitions help control model hallucination, bias, and information relevance.
• Output structuring insight: Knowing how to leverage output formats like lists, tables, or JSON schemas to make responses machine- or human-friendly and formulate the corresponding prompts. This also includes use cases of structuring multi-step reasoning sequences where one prompt’s output becomes another’s input.

NLP/LLM Knowledge

With a sufficient understanding of how large language models operate and the key aspects involved, prompt engineers can better anticipate their behavior:

• Tokens: the chunks of characters that LLMs process, depending on their token capacity. If a prompt exceeds the model’s token limit, the model will truncate the input or fail.
• Context window: the maximum amount of tokens an LLM can process at one time, including both the prompt and the output. This acts as a model’s “operating memory”. 

*Note: Poor prompt design or inadequate estimation of the interaction flow needed to complete a task may lead to situations when you’ll need to re-provide key instructions like specifying the format, tone, or word count. The larger the context window, the more computational resources and processing expenses are needed.

• Probability and sampling: the mechanisms involved when models generate output: they predict a range of probable tokens and then pick those that score the highest. 

*Note: In output generation, LLMs rely on such sampling parameters as temperature, top-k, and top-p that define the response randomness and “creativity”. To make the outputs relevant to the task they are expected to solve, it is important to understand how to control these parameters when prompting.

• Model hallucinations: grammatically correct, fluent, and confident, but factually wrong output a model can produce.

*Note: LLMs don’t know the information or facts in their answers – they only predict the most statistically likely occurrence based on their training set. So if the training data or context doesn’t clearly contain the correct fact or the prompt is not detailed enough, the model can generate a factually incorrect but statistically the most plausible answer. By understanding why and how hallucinations appear, a prompt engineer can come up with a prompting strategy that will reduce the chance of hallucinations.

• Intent recognition: in the same way as LLMs predict tokens in their responses instead of “knowing facts”, they “interpret” requests by finding matching patterns instead of actually understanding them, like humans do.

* Note: This is why the response style and content will be statistically matching a specific request wording or implicit preference (e.g., while the instruction “Explain…” implies detailed reasoning, the word “List…” will imply the answer should be formed in structured bullets). Thus, even small wording changes in prompts can push the model into a completely different probability space and impact the response drastically.

Software Development Skills

Despite its disruptive impact on modern software programming, prompt engineering doesn’t replace coding – it builds upon it. Skilled prompt engineers are software developers by training: they should understand coding principles, integration points, data structures, and system design.

• Programming fundamentals: to correctly embed LLM prompts inside code (for pipelines, data preprocessing, or agent logic). Understanding control structures and data flow is essential for making dynamic, modular prompts.
• API integration: prompts don’t function in isolation — they are used within APIs, applications, or workflows. A prompt engineer must know how to call LLM APIs (OpenAI, Anthropic, etc.), pass parameters, handle rate limits, and integrate with other system components.
• Data handling: AI-enabled systems highly depend on clean, contextual input data. Engineers must know how to prepare, sanitize, and format data for model consumption.
• System design thinking: understanding architecture, modularity, and scalability ensures prompts remain maintainable and interpretable as applications evolve.

Iterative Testing Mindset

In business contexts and generation at scale, prompt engineering is a trial-and-error process of picking the wording that will yield the best result. Just like software developers, prompt engineers continuously come up with and test different hypotheses and approaches to solving a task. This means that prompt crafting requires the skill of systematic work organization:

• Test management and evaluation metrics: the ability to pick representative inputs, organise them into test sets, and measure the results in terms of accuracy, completeness, style consistency, and other relevant criteria.
• Experimentation skills: the ability to project and apply different testing techniques, such as A/B testing, to compare different prompt instructions, like roles, formats, context framing, etc.
• Error analysis: the skill of processing the failed results, such as misinterpreted tasks, hallucinated facts, or broken formatting, and making informed conclusions as to how to adjust the next round of prompts.
• Continuous improvement: this involves not just learning from own mistakes but also actively following external resources for the latest model updates, such as API versions, possible context window changes, etc.

Post-Processing Know-How

Even though post-processing takes place after prompting and output generation, together they make up a single feedback loop “prompt – output – post-processing – prompt refinement.” This means that the ability of a prompt engineer to evaluate and validate the model’s output is key in further transforming it into responses that will eventually meet the business or system requirements. Moreover, post-processing skills are particularly essential to effective prompt engineering when prompts should be further integrated into automated business systems.

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As you can see, while early prompt engineering relied on intuition and experimentation, it’s rapidly evolved into a formalized discipline with tools, frameworks, and best practices. Today’s prompt engineers use prompt templates and parameterization, evaluation frameworks, version control, vector databases, and retrieval-augmented generation (RAG). 

Moreover, prompt engineers sit at the crossroads of several domains:

• Software engineering, for system design and API integration.
• Machine learning, for understanding how models interpret tokens and context.
• Linguistics and psychology, for crafting instructions that shape reasoning and tone.
• Domain expertise, to align AI behavior with specific business logic and compliance needs.

In other words, prompt engineering extends the software engineering toolkit rather than simplifying it. A strong prompt engineer needs as much technical depth as a traditional developer, plus an additional layer of linguistic and cognitive understanding.

Conclusion: From Code to Conversations

Prompt engineering is not replacing programming; it’s transforming it.

Despite a popular misconception, developers use prompts and LLMs not just to generate code but also as an important part of application logic. They help to control reasoning, enforce policies, transform data, drive workflows, and enable decision-making that is impractical or impossible to implement with traditional code. 

A multitude of prompt engineering techniques have evolved, opening more opportunities to what we can achieve using the power of generative AI. Some of them, such as zero-shot or few-shot prompting, can be quite straightforward and widely used, even by non-technical users; others are more complex and require technical insight, understanding of how LLMs function and reason, and certain organizational skills.

This suggests that prompt engineering has already become a full-fledged technical competency that requires background knowledge, mastery, and creativity. In business-oriented applications, it also involves domain expertise, business understanding, and a systemic approach. And our engineers are already nailing it!

At Axon, we excel at transforming our clients’ business ideas into actionable, step-by-step roadmaps to successful technology implementations. We help business decision makers validate their solution hypotheses, select optimal tech stacks, design scalable and maintainable systems, deconstruct plans into technical tasks for developers, and eventually turn Jira backlogs into production-ready applications within expected timeframes and budgets.

Our expertise in full-cycle software development ensures that your vision is met with precision and excellence, from concept to maintenance. Let's drive your business growth and efficiency together. Contact us today and take the first step towards unlocking the full potential of AI for your enterprise.