Basic Prompt Engineering for Local LLMs (Ollama & GPT4All)

Posted on by

You’ve taken the exciting step of running large language models (LLMs) right on your own computer using tools like Ollama or GPT4All. This gives you privacy, control, and the ability to experiment without relying on cloud services. But maybe you’ve noticed that the answers you get aren’t always exactly what you hoped for. They might be vague, miss key details, or simply not follow your instructions precisely. This is where prompt engineering comes in. It’s not about changing the AI model itself, but about learning how to communicate with it effectively. Think of it as learning the AI’s language. Mastering basic prompt engineering techniques is crucial for getting the best possible results from your local LLMs, ensuring you maximize the power of the models you’re running on your hardware. This guide walks you through fundamental techniques to improve your interactions with Ollama and GPT4All.

What is a Prompt? (Simply Defined)

Simply put, a prompt is the input text you provide to an AI model. It’s the question you ask, the instruction you give, or the text you want it to process. Everything you type into the chat box or command line is the prompt that guides the AI’s response.

The Fundamentals of Good Prompts

Regardless of the specific technique you use, several core principles underpin effective prompt engineering:

Be Clear and Specific

Ambiguity is the enemy of a good prompt. The AI doesn’t read your mind; it only processes the text you provide. State your request directly and avoid vague language. Tell the AI exactly what you want it to do and what kind of output you expect.

Provide Sufficient Context

Often, the AI needs background information to generate a relevant and accurate response. This context could be previous parts of a conversation, a document you want summarized, or details about the situation you’re asking about. Giving the AI the necessary information upfront helps it understand the scope and requirements of the task.

Tell the AI What to Do (Instructions)

Use clear instructions with action verbs. Do you want it to “summarize,” “explain,” “write,” “list,” “compare,” or “generate”? Explicitly state the action you want the AI to perform based on your prompt.

Experiment and Iterate

Prompt engineering is an iterative process. Your first attempt might not yield the perfect result. Don’t be afraid to tweak your prompt, add more detail, change the wording, or try a different technique. Learn from the AI’s responses and refine your approach.

Key Basic Prompt Engineering Techniques

Let’s explore some fundamental techniques that can significantly improve the quality of responses from your local LLMs.

Technique 1: Clear Instructions & Role-Playing (Setting the Stage)

The most basic and crucial technique is giving the AI a clear instruction. You can enhance this by assigning the AI a specific role or persona. This helps guide the tone, style, and perspective of the AI’s response.

Explain the importance of clearly stating the task and, optionally, assigning the AI a role or persona.

Example Structure: “Act as [Persona]. Your task is to [Clear Task].”

Code Examples:

Simple Instruction:

Write a short paragraph about the benefits of reading.

With Persona:

Act as a helpful librarian. Write a short paragraph about the benefits of reading, encouraging someone to visit the library.

Notice how adding the persona “helpful librarian” in the second prompt provides a clear instruction (“Act as a helpful librarian”) that shapes the expected output. The AI is likely to use encouraging language and perhaps mention resources available at a library, whereas the first prompt might generate a more general, factual list of benefits. By setting the stage with a role and a clear task, you give the AI a specific framework to generate its response.

Technique 2: Providing Context (Giving Background Info)

LLMs rely heavily on the information provided in the prompt. Giving them context is essential, especially when your request relates to specific information or a particular scenario. Without context, the AI might hallucinate information, provide generic answers, or simply state it doesn’t have enough information.

Explain how context helps the AI generate relevant and accurate responses.

Example Structure: “[Background Information]. Based on the above, [Clear Task related to info].”

Emphasize putting context *before* the instruction.

Code Examples:

Prompt without context:

Summarize the meeting.

This prompt is impossible for the AI to answer usefully because it has no context about *which* meeting you mean or where the notes are.

Prompt with context:

Here are the notes from our morning team meeting:
Attendees:
 John, Sarah, MikeTopics Discussed:
 Q3 Budget Review, New Project Proposal, Marketing Campaign Update

Decisions:
- Approved Q3 budget with minor adjustments.
- Decided to proceed with the New Project Proposal, pending final scope definition.
- Agreed on key messaging for the Marketing Campaign Update.

Action Items:
- Sarah to finalize budget adjustments by EOD Friday.
- John to draft project scope document by Monday.
- Mike to prepare presentation on campaign metrics for next week.

Based on the above, summarize the key decisions and action items from these notes.

In the second example, you provide the full meeting notes as context *before* asking for the summary. The instruction (“Based on the above, summarize…”) explicitly tells the AI to use the provided text. This allows the AI to extract the specific information you need (decisions and action items) directly from the source material you gave it, resulting in a relevant and accurate summary tied to your specific meeting.

Technique 3: Using Examples (Few-Shot/One-Shot Prompting)

Sometimes, the best way to show the AI what you want is to demonstrate it. Few-shot or one-shot prompting involves giving the AI one or more examples of the desired input-output pair before asking it to perform the task on your actual input. This technique is particularly powerful for tasks requiring a specific format, style, or data extraction pattern.

Explain that showing the AI an example of the desired input/output format or style is very powerful, especially for specific tasks like formatting or data extraction. (Keep ‘Few-shot’ simple – just means one or more examples).

Example Structure: “[Instruction]. Example: Input: [Example Input 1] Output: [Example Output 1]. Now, Input: [Your Input] Output:”

Code Examples:

Instruction + One Example (One-Shot):

Convert the following product descriptions into tweet-sized summaries (under 280 characters):
Example:
Input: "Our new noise-canceling headphones offer incredible sound quality and comfort for hours of listening."

Output: "Immerse yourself! ✨ New noise-canceling headphones deliver amazing sound & comfort. #Audiophile #Headphones"

Now, Input: "This book is a thrilling mystery novel set in a remote cabin during a snowstorm, perfect for cozy nights."

Output:

By providing the first “Input” and “Output” pair, you show the AI the desired format (short, engaging text suitable for Twitter), style (using emojis, hashtags), and length constraint (tweet-sized). When you then provide “Now, Input:”, the AI understands the pattern from the example and attempts to apply it to your new input. This is much more effective than simply saying “Summarize this book description for Twitter,” as the example clarifies *how* you want it summarized and formatted.

Technique 4: Specifying Output Format & Constraints

Often, you don’t just want information; you want it presented in a specific way. You can explicitly tell the AI the desired output format (e.g., list, table, paragraph) and add constraints (e.g., length, keywords to include, tone). This helps structure the AI’s response to make it immediately useful for your purpose.

Explain how to ask for the response in a specific format or with specific constraints.

Example Structure: “[Instruction]. Format: [Desired Format]. Constraints: [List Constraints].”

Code Examples:

Format + Constraint:

List 5 benefits of learning a new language.Format: Bullet points.
Constraint: 
Each point should be only one sentence.

This prompt is very clear. It gives the main instruction (“List 5 benefits…”), specifies the structure (“Format: Bullet points”), and adds a rule for each item in the list (“Constraint: Each point should be only one sentence”). The AI is much more likely to provide a response that looks exactly like this:

*   Enhances cognitive skills.
*   Improves memory and concentration.
*   Boosts career opportunities.
*   Facilitates cultural understanding.
*   Makes travel more enriching.

Another example:

Explain the concept of recursion in programming.
Format:
 Simple paragraph, aimed at a beginner.
Constraint:
 Use an analogy.

Here, you guide the AI on the complexity level (“aimed at a beginner”), the structure (“Simple paragraph”), and require a specific creative element (“Use an analogy”). This helps ensure the output is not overly technical and is easy for a novice to grasp, potentially using an analogy like Russian nesting dolls or mirrors facing each other.

Applying Prompt Engineering in Ollama and GPT4All

The good news is that applying these techniques in your local Ollama or GPT4All setup is straightforward because they primarily involve how you structure the text you input.

In Ollama’s command-line interface (CLI), you type your prompt directly after the >>> symbol. When you hit Enter, the AI processes the entire block of text you’ve written. To use the techniques discussed:

  • For clear instructions, context, examples, or format/constraints, simply type out your carefully constructed prompt before pressing Enter. Include all the necessary components in your text input.
  • A multi-line prompt is perfectly fine. You can type multiple lines of context or instructions before the final command.

Example in Ollama CLI:

>>> Act as a travel guide for Rome. List 3 must-see historical sites.
Format:
 Numbered list.
Constraint:
 Include a brief reason for each.

In the GPT4All graphical user interface (GUI), you type your prompt into the chat input box at the bottom of the window. When you click send or hit Enter, the text in the box is sent to the AI. Applying techniques involves:

  • Typing your clear instruction, context, examples, or format/constraint requirements directly into the chat box.
  • You can type multi-paragraph prompts in the chat box before sending.

Example in GPT4All GUI (text typed into the input box):

Here is a product review:
 "The battery life on this laptop is terrible, it only lasts about 2 hours on a full charge. Performance is okay for basic tasks, but it struggles with anything demanding. The screen is bright though.
"Based on the review, identify the main positive and negative points mentioned.
Format:
 Bullet points under "Positives" and "Negatives" headings.

In both tools, the fundamental principles of structuring your text input are identical. The techniques are about crafting the message you send to the AI, not about using specific commands within the tool interfaces themselves (though some models might support special tokens, these basic techniques work across most models).

Tips for Prompting Local LLMs

Working with local LLMs like those in Ollama and GPT4All has a few unique considerations:

  • Model Size Matters: The capabilities of the AI model you’ve downloaded significantly impact its ability to follow complex instructions or understand subtle nuances. Smaller models (e.g., 3B or 7B parameters) may require *more* explicit instructions, simpler language, and clearer context than larger ones (e.g., 13B or 30B+). Be extra clear with smaller models.
  • Hardware Impact: The speed at which the AI generates a response depends on your computer’s hardware (CPU, RAM, GPU). Responses might be slower than cloud services, especially with longer or more complex prompts or larger models. Be patient, particularly when iterating on prompts.
  • Experiment with Models: Different models (even of the same size) have been trained on different data and may respond differently to the same prompt. If a technique doesn’t work well with one model, try switching to another one available in Ollama or GPT4All to see if you get better results.
  • Experiment with Prompts: Don’t get discouraged if a prompt fails. Tweak it! Change a word, add more context, simplify the request, or try a different technique. Practice is key.

Troubleshooting Basic Prompting Issues

You’ll inevitably encounter times when the AI’s response isn’t what you expected. Here are some common issues and what they likely mean:

  • Vague or Irrelevant Answers: The AI didn’t understand your request clearly. Your prompt was likely not specific enough. Add more detail and clarify exactly what you want.
  • Missing Information: The response lacks details you expected. You likely didn’t provide necessary context. Include all relevant background information in your prompt.
  • Ignoring Instructions or Constraints: The AI didn’t follow your formatting request or ignored a negative constraint (like “do not include X”). The prompt might be too complex for the model, or the model might struggle with negations or complex rules. Try simplifying the request, breaking it into smaller steps, or restating the constraint positively if possible.
  • Repetitive or Nonsensical Output: This can sometimes happen with smaller or less stable models, especially with very long or complex prompts. Try simplifying the prompt or switching to a different model.
  • Slow Responses: As mentioned, this is often due to your hardware or the model size. Ensure your system meets the recommended specs for the model you’re running.

FAQs

Here are answers to a few common questions about prompt engineering for local LLMs:

Is prompt engineering different for local vs. cloud AI?

The fundamental principles of prompt engineering (clarity, context, instructions, examples, format) are the same for both local and cloud AI. However, with local LLMs, you might need to be more explicit and simplify prompts due to potential limitations of smaller models or less powerful hardware compared to large commercial cloud APIs.

Do specific models need different prompts?

Yes. While basic techniques work across the board, models are trained differently. Some models might be better at creative writing, others at coding, and some might follow instructions more rigidly. Experimentation is key to understanding how a specific model in Ollama or GPT4All responds to different prompt styles.

Can prompt engineering fix factual errors?

No. Prompt engineering helps you guide the AI to use the information it *already knows* (or that you provide as context) in a specific way. It cannot correct factual inaccuracies or make the model knowledgeable about things it wasn’t trained on. For factually accurate information, you should verify the AI’s output.

Are there advanced techniques?

Yes, there are more advanced techniques like Chain-of-Thought prompting (asking the AI to “think step-by-step”), Tree-of-Thought, or using specific prompting frameworks. However, the techniques covered in this guide are the essential building blocks for getting good results from your local LLMs for most common tasks.

Conclusion

Running local LLMs with Ollama and GPT4All gives you incredible power and flexibility. By learning basic prompt engineering, you unlock the full potential of these models. Techniques like providing clear instructions, adding essential context, using examples to show what you want, and specifying the output format can dramatically improve the quality and relevance of the AI’s responses. Don’t be afraid to experiment with different prompts and models. Open up your Ollama terminal or GPT4All application and start practicing these techniques today. The more you practice, the better you’ll become at communicating with your AI, turning frustrating, vague answers into helpful, tailored responses.