6 Advanced Prompting Techniques For Enhanced Workflows

Make AI Really Work For You

I am obsessed with finding new ways to prompt generative AI effectively. Many think it is as simple as asking a question, but they are often disappointed with the response.

The way you ask the question and prompt the AI interface is the difference between a basic, uninspiring response and a detailed, nuanced and powerful response that does more than just answer your question.

These are six prompting techniques I’ve been using to maximise the value I get from AI conversations.

Memory-of-Thought

Technique

Memory-of-Thought leverages previous outputs to inform the current response, ensuring that the reasoning is consistent and builds upon prior insights. This approach is particularly useful in scenarios where continuity and coherence across multiple steps or stages of analysis are crucial. By referencing past outputs, the model can avoid redundant calculations or conflicting recommendations, streamlining decision-making processes.

Example

When forecasting quarterly revenue, the model uses previous outputs related to client acquisition trends, past revenue streams, and market growth data. By referencing these past outputs, the model ensures that the revenue projections are consistent with historical trends and any recent changes in the market, providing a more accurate and reliable forecast.

Few-Shot-CoT (Chain-of-Thought)

Technique

Few-Shot-CoT combines the principles of few-shot learning (where the model is given a few examples to learn from) with chain-of-thought reasoning, which involves breaking down a problem into a series of logical steps. This technique is particularly effective when dealing with complex problems that require structured thinking and reasoning through multiple stages or factors.

Example

A health tech company is planning to expand its operations into a new region. The model is provided with a few examples of past expansion efforts, including steps such as market analysis, regulatory compliance, and partnership development. Using these examples, the model reasons through the process of expansion, generating a detailed, step-by-step plan that addresses potential challenges in the new market, such as local competition and healthcare regulations.

Step-Aware Verification

Technique

Step-Aware Verification involves the model carefully checking each step of the reasoning or solution process to ensure accuracy. This is particularly useful in domains where errors at any stage of analysis can lead to significant issues down the line. The model’s ability to self-verify its steps enhances the reliability of the final output.

Example

When auditing the financial performance of a health management organization, the model verifies each step of the audit process, from data collection and classification to the final reconciliation of accounts. This ensures that every stage is accurately completed, reducing the risk of errors in the final financial report and providing stakeholders with confidence in the results.

Demonstration Ensembling

Technique

Demonstration Ensembling involves combining multiple demonstrations or examples to create a more comprehensive and robust prompt. By drawing on diverse examples, the model can develop a deeper understanding of the problem and produce more reliable and well-rounded responses.

Example

A company is developing a strategy for a new product launch. By using Demonstration Ensembling, the model combines multiple examples of past product launches, considering factors like marketing strategies, competitor actions, and market response. This approach helps the model generate a more robust and comprehensive launch plan that is tailored to the unique aspects of the current market, ensuring a higher chance of success.

Self-Generated ICL (In-Context Learning)

Technique

Self-Generated ICL allows the model to use its own previous outputs as new examples or context for further reasoning. This iterative approach enables the model to refine its understanding and responses over time, learning from its own outputs to improve subsequent reasoning.

Example

A health tech company is developing an AI-driven platform to optimize resource allocation for remote health monitoring devices. Initially, the model generates a basic resource allocation strategy based on current device usage patterns and existing resource constraints. The model then uses this initial strategy as an in-context example to refine and optimize the allocation process further. It iterates by incorporating real-time data from device usage, supply chain fluctuations, and emerging health trends (e.g., increased monitoring due to a new health scare). This iterative process allows the company to continuously improve resource allocation, ensuring that devices are always available where they are most needed, thus enhancing service delivery and operational efficiency.

Question Decomposition

Technique

Question Decomposition breaks down complex or multi-faceted questions into simpler, more manageable sub-questions. This technique is particularly useful in situations where a problem is too large or complex to be tackled in one step. By addressing each sub-question individually, the model can systematically work towards a comprehensive solution.

Example

A health tech company is developing an AI-powered analytics platform to assist hospitals in optimizing their IT infrastructure. The broad question posed is “How can we enhance the efficiency and reliability of hospital IT systems?” The model uses Question Decomposition to break this down into smaller, more manageable sub-questions such as:

• “How can we improve data storage and retrieval speeds?”
• “What steps can be taken to ensure data security and compliance with healthcare regulations?”
• “How can we minimize system downtime and improve uptime reliability?”
• “What are the most cost-effective ways to upgrade legacy systems?”

By addressing each sub-question individually, the model provides specific recommendations for each aspect, such as upgrading to faster, more secure cloud storage solutions, implementing stronger encryption protocols to comply with HIPAA, optimizing server maintenance schedules to reduce downtime, and identifying cost-effective software upgrades. This systematic approach helps the health tech company deliver a comprehensive solution that significantly improves the IT infrastructure in hospitals, leading to better overall performance and data security.

An AI Co-Pilot Built For You

Animus AI is an AI copilot built for knowledge workers in the health sector. A place where you can apply these prompts to a model that is customised to provide focused, intuitive and reliable responses.

Get more out of your AI experience with Animus AI — Animus Health (animus-health.com)