Your guide to prompting AI Assistants and Agents

I’m working on an intensive course for “No-Code Builders”. As part of it, I’ve added a new module which I think is critical for everyone who considers or already builds AI stuff for personal or business use: What are the differences, templates, and best known practices for prompting AI assistants and agents? 

TL.DR: Best practices and templates for prompting in general, prompting assistant, agents, and a comparison between the three.

Quick alignment: Why good prompting matters and some universal best practices. 

You might have heard contradictory claims: Expert A: “Prompt Engineering is the single most important skill you need to master in the AI era.” Expert B: “Prompt Engineering is overrated and is becoming obsolete as the models become smarter and resolve most issues for which we had to engineer our prompts inherently.”

Wait, what?

So what should you do?

Perhaps it’s the “engineering” part that throws us into a loop. Being able to clearly express to the model what we want it to do, what the context is for the request, and what the constraints are for the output will never become obsolete.  

Here’s an analogy that I like to use to motivate us to improve our articulation of what we want: Think of Large Language Models (LLMs) as the world’s most extensive library. One that holds literally all the human knowledge (and then some). You wouldn’t go into the library asking the librarian: “Can you give me a book about monkeys?” and expect her to know that no, it’s not “Curious George” you are after, but in fact, that you’re an expert biologist conducting an extensive research about owl monkeys and their mating rituals. If you want the librarian to direct you to a very specific section, shelf, and even book, you’ll need to tell them exactly what your professional background, interests, and specific needs are. As magical LLMs seem at times, they, too, cannot read your mind. You need to help direct them towards their “section, shelf, and book” so they can retrieve the knowledge or outcome you are after.  

Since I know many of you are comfortable with “basic/ Ad-hoc” prompt engineering (or prompting..), I will just put some of my best advice on this topic and move on to prompting assistants and agents.

My top 5 prompting golden rules:

1. Spending time prompting always has a positive ROI

2. Context is the queen of getting you to where you want faster

3. The best analogy is to think about how you would explain the assignment to a brilliant, overly eager-to-please intern: Provide instructions that are clear, analytical, step-by-step (especially when it’s complex), detailed (but without unnecessary information), and set the expectation about the expected output. 

4. Anthropomorphism generally works; most human-to-human communication skills work well on models.

5. Iterate as needed, but not as a means for being too lazy to start with a good prompt.   

Some additional prompting best practices: 

• Clear & Specific: Clearly define tasks, format, and detail.

• Context & Examples: Guide model behavior with explicit examples.

• Structured Prompting: Use structured instructions (numbered lists, steps).

• Few-shot Prompting: Embed 2–5 examples in prompts.

• Iterative Refinement: Regularly test and adjust prompts.

Additional Important Considerations:

• Instruction Fatigue: Overly long or repetitive instructions can cause model drift or performance degradation.

• Sensitivity to Model Version: Model upgrades or changes can significantly impact prompt effectiveness; validate prompts regularly.

• Dynamic Prompting (relevant for building AI flows and not ad-hoc prompting): Adjust prompts on-the-fly based on context or user input to improve output quality and flexibility.

Now that I’ve cleared this, let’s move on to talk about prompting assistants and agents:

Wait, isn’t prompting just prompting, regardless of what the AI type is? 

Well, yes. And no. The basic golden rules apply to any type of prompting. Whether it’s ad-hoc (a.k.a talking to GPT, Claude, Gemini, and the likes), or when creating the instructions for a custom GPT/ Gems (assistant) or an AI agent you’re building. However, the purpose and the role the prompt plays are different. 

Think of it as a spectrum of autonomy and complexity:

• Basic Prompting (e.g., to a raw model): Direct instruction for an immediate task.

• AI Assistants: More structured, persistent, tool-using entities guided by a system prompt and user interactions.

• AI Agents: Designed for higher autonomy, goal-seeking, and complex task execution with less step-by-step guidance.

Prompting AI assistant: Persistent power & control

AI Assistants (like custom GPTs, Assistants API, or similar persistent AI tools) offer a more continuous and context-aware interaction than ad-hoc prompting. They can maintain memory across a conversation (thread), utilize tools, and adhere to overarching instructions.

The Two Core Prompt Components for Assistants:

1. System Prompt / Instructions (The "DNA"):

2. User Prompts / Messages (The "Tasking"):

Creating Effective Assistant Instructions (System Prompt Templates & Reusable Components): Structure your System Prompt for clarity and reusability. Think in terms of "building blocks"

Assistant prompt template:

## 1. ROLE & PERSONA ##

You are [ASSISTANT_NAME], a specialized AI assistant for [DEPARTMENT/TEAM/PURPOSE].

Your persona is: [e.g., Friendly and supportive, Formal and precise, Creative and inspiring].

You interact with: [e.g., Internal employees, New customers, Technical experts].

## 2. CORE DIRECTIVE & GOAL ##

Your primary objective is to [e.g., answer questions based on the provided 'XYZ_manual.pdf', help draft marketing copy, summarize research papers, schedule meetings using the 'CalendarTool’].

You should always strive to [e.g., provide actionable advice, ensure accuracy, offer multiple options].

## 3. KNOWLEDGE & TOOLS ##

(If applicable) You have been provided with the following documents for your knowledge base:

-[Document 1 Name/Description]

-[Document 2 Name/Description]

(If applicable) You have access to the following tools:

-[Tool 1 Name (e.g., Retrieval)]: Use this for [Purpose, e.g., searching the provided documents].

-[Tool 2 Name (e.g., Code Interpreter)]: Use this for [Purpose, e.g., analyzing data, creating charts].

-[Tool 3 Name (e.g., Custom Function - 'GetStockPrice')]: Use this for [Purpose].

-

## 4. RESPONSE GUIDELINES & CONSTRAINTS ##

-Language & Tone: [e.g., Professional English, Use empathetic language].

-Formatting: [e.g., Use markdown for lists. Present comparisons in a table. Keep summaries to 3 bullet points].

-Limitations:

-You MUST NOT [e.g., provide medical advice, share confidential company strategy].

-You SHOULD AVOID [e.g., speculating, using jargon without explanation].

-Mandatory Actions:

-ALWAYS [e.g., cite the source document if using Retrieval].

-If a request is ambiguous, ALWAYS [e.g., ask clarifying questions before proceeding].

## 5. EXAMPLE INTERACTIONS (Optional, but very helpful for complex behaviors) ##

User: [Example of a typical user query]

You: [Ideal response demonstrating desired persona, tool use, and formatting]

Additional Best Practices & Tips for Prompting Assistants:

• Be Explicit & Unambiguous: Clearly define the assistant's role, what it should do, and equally important, what it should not do in the System Prompt.

• Iterate and Refine: Your first System Prompt is rarely perfect. Test the assistant with various user prompts and refine its instructions based on its behavior.

• Specificity is Your Friend: Vague instructions lead to vague or unpredictable behavior.

• Front-load Important Instructions: Place the most critical instructions at the beginning of the System Prompt.

• Manage Context Length: While assistants have memory, be mindful that extremely long conversations can still hit limits or dilute focus. Summarize or reset if needed.

• Clearly Define Tool Usage: If your assistant uses tools (Retrieval, Code Interpreter, Functions), explain when and how it should decide to use them.

• Provide Examples in Instructions: Showing the assistant an example of a good interaction (user prompt + desired assistant response) within the System Prompt can be very effective for complex behaviors.

• Error Handling Guidance: You can instruct the assistant on how to respond if it can't fulfill a request or encounters an error.

  
  

Prompting AI agents: Strategic goals & autonomous tasking

When building AI agents, there is a broader range of tools and constraints/ formats they bring with them. Thereby, prompting agents means, first and foremost, you need to follow the tool or framework requirements and then add your specifics. For example, if you use CREW AI to build multi-agent systems, it has a very particular structure for guiding your agents. Other tools break down the agent configurations into building blocks, and you’ll need to prompt within these constraints. The guidelines and best-known methods below do not refer to or assume any specific tool or framework. I’m trying to give overall guidelines that will express the motivation and key components or considerations. To keep it simple, I’ve also deliberately not gone into the specific planning/ architectures of agents (like ReAct or other methods)

Primary Goal of Prompting (or "Configuration") of an agent:

• To define a high-level objective or mission the agent needs to achieve.

• To specify the resources, tools, and permissions the agent has to work with.

• To set constraints, boundaries, and success criteria.

• Potentially, to define a planning or reasoning strategy (e.g., "use a ReAct framework," "always verify information from source Y before taking action Z").

Level of Detail in Initial Prompt/Configuration: This can be high in defining the environment and goal, but often less about prescribing the exact steps. You're defining the "what" and "why," and providing the "how" in terms of capabilities, letting the agent figure out the sequence.

User Interaction Style: Potentially less direct, turn-by-turn intervention. More about:

• Launching the agent with a mission.

• Monitoring its progress.

• Providing feedback or course correction if it gets stuck or goes off-track.

• Receiving a completed outcome or report.

Focus of Initial "Prompt"/Setup: Defining complex, multi-step goals that require the agent to plan, make decisions, use tools autonomously, and potentially self-correct.

Example of "Agent Configuration" thinking:

• Goal: "Research and compile a comprehensive report on the market viability of launching a new sustainable coffee pod in the EU, considering competitor analysis, consumer trends, and potential regulatory hurdles. Deliver a 10-page report with a summary by next Friday."

• Tools/Resources: Access to web search, financial databases, internal sales data API, document generation tool.

• Constraints: Budget for research: $X. Focus on trends from the last 2 years.

• Success Criteria: Report includes quantifiable data, clear recommendations, and an executive summary.

User "Interaction" with the Agent (after launch): "What's your current progress on the market report?" or "Prioritize competitor analysis for German-speaking countries first."

A template for an AI agent prompting

As mentioned before, in most cases, you will not configure an agent in one prompt and use a free-style manner. However, the prompt below gives you a good overview of everything you might want to configure:

AI Agent Configuration & Mission Briefing Template

Agent Name/ID: [Assign a unique identifier or descriptive name, e.g., "MarketResearchAgent_Q3_Pharma"]

Version: [e.g., 1.0] Date Created: [Date] Mission Lead/Overseer: [Your Name/Team]

1. OVERARCHING MISSION & GOAL(S):

• Primary Objective: [Clearly state the ultimate goal the agent is meant to achieve. Be as specific and measurable as possible. E.g., "Generate a comprehensive market analysis report on the viability of launching Product X in Southeast Asia by [Date], identifying key competitors, target demographics, regulatory hurdles, and providing a risk assessment with a recommended go/no-go decision."]

• Secondary Objectives (if any): [List any supporting goals. E.g., "Compile a database of all potential distributors in the region.", "Monitor news sentiment regarding Product X category for the duration of the mission."]

• Success Criteria: [How will success be measured? What defines a completed mission? E.g., "Delivery of a 20-page report fulfilling all aspects of the Primary Objective.", "Accuracy of competitor data >95%.", "All identified risks have mitigation suggestions."]

• Mission Deadline/Timeline: [Specific end date or phases with milestones. E.g., "Initial competitor list by [Date 1], Draft report by [Date 2], Final report by [Date 3]."]

2. AGENT ROLE & OPERATIONAL PERSONA (Optional, but can guide behavior):

• Role: [e.g., "Lead Market Analyst," "Automated Project Scout," "Due Diligence Investigator"]

• Tone (if interacting or generating reports): [e.g., "Formal and objective," "Data-driven and concise," "Cautiously optimistic"]

3. CORE CAPABILITIES & AUTHORIZED TOOLS:

• [List each tool/API/function the agent is permitted to use and its purpose.]

• Tool 1: [e.g., Web Search Engine Access (Specify if restricted to certain sites/domains)]

• Tool 2: [e.g., Internal Document Repository API (Specify folders/tags)]

• Tool 3: [e.g., Data Analysis Module (e.g., Code Interpreter with Python libraries like Pandas, NumPy)]

• Tool 4: [e.g., Report Generation Module (e.g., ability to structure and write into a document)]

• Tool 5: [e.g., Communication API (e.g., Slack, Email - USE WITH EXTREME CAUTION & STRICT RULES)]

4. KNOWLEDGE SOURCES & DATA ACCESS:

• Pre-loaded Data: [List any datasets, documents, or knowledge bases provided to the agent at the start. E.g., "Initial list of known competitors (competitors_v1.csv)", "Company's strategic plan for Asia (strategy_doc.pdf)"]

• Authorized External Data Sources: [Specify any external databases, websites, or APIs it should prioritize or is allowed to access beyond general web search. E.g., "Statista," "PubMed," "Financial Times API"]

• Data Handling Protocols: [e.g., "All collected data must be stored in [Specified Location/Format].", "PII must not be stored unless explicitly approved and anonymized."]

5. OPERATIONAL STRATEGY, HEURISTICS & PRIORITIES:

• Overall Approach: [Suggest a high-level strategy if applicable. E.g., "Prioritize identifying the top 5 competitors first.", "Focus on quantitative data before qualitative insights.", "Employ a ReAct (Reason + Act) framework for task decomposition."]

• Information Validation: [e.g., "Cross-verify critical information from at least two independent sources.", "Flag unverified information in reports."]

• Decision-Making Guidelines: [If the agent needs to make choices. E.g., "If multiple data sources conflict, prioritize official government sources or peer-reviewed publications.", "Optimize for cost-efficiency when using paid API calls."]

• Task Prioritization: [e.g., "Address regulatory hurdles with highest priority.", "Complete tasks related to the primary objective before secondary ones."]

6. CONSTRAINTS, BOUNDARIES & ETHICAL GUIDELINES:

• Budget Limits (if applicable): [e.g., "Maximum API call costs: $X.", "Maximum compute time: Y hours."]

• Resource Limits: [e.g., "Max number of web pages to crawl per source: Z"]

• "Off-Limits" Topics/Actions: [Clearly state what the agent MUST NOT do. E.g., "Do not attempt to access systems not explicitly listed in Tools.", "Do not engage in any deceptive practices.", "Do not generate content that is discriminatory or harmful."]

• Data Privacy: [e.g., "Adhere to GDPR/CCPA guidelines.", "Anonymize all personal data before analysis."]

• Legal Compliance: [e.g., "Ensure all information gathering is compliant with copyright laws."]

7. REPORTING, LOGGING & HUMAN REVIEW POINTS:

• Progress Reporting Frequency: [e.g., "Daily summary to Mission Lead.", "Milestone completion alerts."]

• Logging Requirements: [e.g., "Log all actions taken, tools used, sources accessed, and decisions made with timestamps."]

• Mandatory Human Review Points: [Specify critical junctures where the agent must pause and seek human approval before proceeding. E.g., "Before finalizing budget allocation for external data purchase.", "Before initiating any external communication (if Tool 5 is enabled).", "Before submitting the final draft of the report."]

• Error Handling & Escalation: [What should the agent do if it encounters repeated failures, ambiguity it cannot resolve, or reaches a constraint? E.g., "If a tool fails 3 times consecutively, log the error and notify Mission Lead.", "If mission objectives conflict, request clarification from Mission Lead."]

8. CONTINGENCY PLANS (Optional):

• If Primary Tool Fails: [e.g., "If primary web search API is down, switch to secondary search provider."]

• If Critical Data Source is Unavailable: [e.g., "Note unavailability in report and proceed with available data, highlighting the gap."]

• …

Using this template and general agent prompting best practices:

• Be Explicit: The more detail, the better the agent will likely perform and stay within bounds.

• Iterate: Your first configuration might not be perfect. Be prepared to monitor the agent and refine this briefing.

• Start Simple: For initial tests, use a subset of these fields or simpler instructions, then build complexity.

• Scope Management: Clearly define the scope to prevent the agent from attempting tasks beyond its capabilities or your intentions (you can also consider a multi-agent alternative, but this is a discussion for another day).

• Safety First: Pay special attention to constraints, error-handling, “do-no-harm” (to your data, business, brand, finances, etc.), and human review points, especially if the agent can act in the real world (e.g., send emails, spend money).

Putting it all together and final remarks

Here’s a table summarizing the key themes I discussed:

Whether you’ve had extensive experience in using and building AI capabilities or you’re getting there, the muscle of articulating to the models what you want is one that we will forever need to continue flexing. 

Good luck and prompt on!

Before you go:

1. Found this useful? Have requests for other topics you want me to cover? Let me know in the comments..

2. Want a no-code builders course for your organization as well? Talk to me!

3. Have an amazing tip or trick to share? Post it below

* Models and tools I used when compiling this article:

• OpenAI O3, 4.1, ImageGen

• Claude Opus 4

• Gemini 2.5 Pro

• Napkin.ai

• My own experience and brain 🙂