Prompting That Works in 2025 and Beyond

Opening: Sander shares his perspective on AI alignment and the potential risks associated with misaligned AI agents. Quote:

"But more recently, I have become a believer in this misalignment problem... and that is one of the things that has me super concerned."

Takeaway:

• AI alignment is a critical challenge.
• Misaligned AI agents can pose significant risks.
• Consider alignment issues in AI product development.

Opening: Sander emphasizes the importance of including additional information in prompts to improve AI outputs. Quote:

"Including a lot of information just in general about your task is often very helpful."

Takeaway:

• Provide detailed context in your prompts.
• Additional information aids AI understanding.
• Improves accuracy and relevance of outputs.

Opening: Sander introduces ensemble prompting as an advanced technique to enhance AI performance. Quote:

"Ensembling techniques will take a problem, and then you'll have like multiple different prompts that go and solve the exact same problem... and then you take the answer that comes back most commonly."

Takeaway:

• Use multiple prompts or models on the same task.
• Combine outputs to improve accuracy.
• Ensemble prompting can enhance AI product reliability.

Opening: Sander discusses the limitations in completely preventing prompt injection attacks. Quote:

"It is not a solvable problem... It has to be the AI research labs... It has to be innovations in model architectures."

Takeaway:

• Prompt injection can't be fully eliminated.
• Solutions require innovations from AI research labs.
• Focus on mitigation and ongoing vigilance.

Opening: Sander highlights common but ineffective defenses against prompt injection in AI products. Quote:

"The most common technique by far that is used to try to prevent prompt injection is improving your prompt... This does not work, this does not work at all."

Takeaway:

• Simply refining prompts doesn't prevent prompt injection.
• Guardrails and keyword blocking are insufficient.
• AI teams need more robust security measures.

Opening: Sander explains the risks of prompt injection and its impact on AI product security. Quote:

"Getting AI's to do or say bad things... It is not a solvable problem. That's one of the things that makes it so different from classical security."

Takeaway:

• Prompt injection can lead AI to produce harmful outputs.
• This challenge can't be fully eliminated.
• AI teams must continuously monitor and mitigate risks.

Opening: Sander discusses the relevance of chain-of-thought prompting with newer reasoning models. Quote:

"Generally not so useful anymore, because as you just said, there's these reasoning models that have come out, and they by default, do that reasoning."

Takeaway:

• Chain-of-thought prompting may be unnecessary with modern models.
• New AI models often reason by default.
• Consider if this technique benefits your AI application.

Opening: Sander debunks the misconception that role prompting enhances AI performance on accuracy tasks. Quote:

"But from my perspective, roles do not help with any accuracy-based tasks whatsoever."

Takeaway:

• Role prompting doesn't enhance accuracy in AI outputs.
• Avoid relying on roles for improving performance in AI products.
• Focus on more effective prompt engineering techniques.

Opening: Sander shares his top prompt engineering technique that significantly boosts AI model performance. Quote:

"If there were one technique that I could recommend people, it is few shot prompting, which is just giving the AI examples of what you want it to do."

Takeaway:

• Implement few-shot prompting by providing examples.
• Examples guide AI models to produce better outputs.
• Enhance your AI product's performance using this technique.

Opening: Sander discusses the ongoing relevance of prompt engineering in AI development, despite claims that it's becoming obsolete. Quote:

"People will kind of always be saying it's dead or it's going to be dead with the next model version, but then it comes out and it's not."

Takeaway:

• Prompt engineering remains crucial in AI product development.
• Continuous experimentation with prompts enhances AI performance.
• Don't assume newer models eliminate the need for prompt optimization.