Prompt Engineering

ChatGPT 4 as a Co-Pilot to Salesforce innovation.


Ian H Smith

As we are in the frenzied period of maximum hype in relation to all things Artificial Intelligence (AI), I am writing a series of blog posts where I am using ChatGPT 4 and applying Prompt Engineering to solve a range of challenges in implementing Salesforce Sales Cloud.

This is not an integration of GPT with Salesforce (future release: Einstein GPT) but a focus on ChatGPT 4, the latest version released by Open AI. Our focus here is business; specifically the challenges faced in everyday life by people engaged in a high-value, high-touch sell. In this context we treat Chat GPT 4 as the Co-Pilot in learning how to increase sales effectiveness.

The content in this blog post is mostly generated from my Co-Piloting with Chat GPT 4, with my adjustments to either refining the prompt or manually editing the final answers.

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Prompt Engineering in the context of a high-value, high-touch sell refers to the strategic formulation of prompts or queries to elicit the most effective and useful responses from advanced AI systems, such as Chat GPT 4, particularly in enhancing sales processes and customer interactions.

1. Role of AI in Sales: 

AI tools, like ChatGPT 4, can assist in various stages of the sales process, from lead generation to closing deals. They can analyse vast amounts of data, provide insights, generate leads, personalise communication, and even predict customer needs.

2. What is Prompt Engineering?: 

Prompt Engineering is the skill of crafting questions or prompts to an AI system to generate the most relevant and useful responses. It involves understanding how the AI interprets and processes language, and using this knowledge to frame prompts that lead to desired outcomes.

3. Application in a High-Value Sell:

This sales approach focuses on significant transactions, often involving complex solutions and a substantial investment from the client. The 'high-touch' aspect implies a personalised, consultative approach, where building relationships and understanding customer needs is paramount.

ROI Modelling: This is where ChatGPT can act as a Co-Pilot, creating ROI Models which buyers can provide a Buyside Forecast to compare with a Sellside Forecast.

Lead Qualification and Prioritisation: Crafting prompts to identify and prioritise high-potential leads from a large dataset.

Personalised Communication: Generating customized emails or messages that resonate with specific clients, using prompts that incorporate customer-specific data.

Market Analysis and Insights: Asking the AI to analyse market trends and provide insights relevant to a particular customer or industry.

Proposal Generation: Creating detailed, tailored proposals by prompting the AI with specific customer needs, industry specifics, and solution parameters.

Handling Objections: Using AI to simulate various customer objections and crafting prompts to find the best counter-arguments or reassurances.

5. Skills Required in Prompt Engineering:

Linguistic Precision: Understanding how different phrasings can lead to varied AI responses.

Contextual Awareness: Framing prompts that take into account the customer's industry, history, and specific needs.

Creativity: Thinking 'outside-of-the-box' to ask AI for insights or data analyses that might not be immediately obvious.

Technical Understanding: Knowing the capabilities and limitations of the AI tool being used.

6. Benefits:

Efficiency: Streamlining the sales process by quickly generating high-quality, relevant outputs.

Personalisation: Enhancing customer engagement through highly tailored communications and solutions.

Data-Driven Decisions: Leveraging AI's data processing capabilities for more informed decision-making.

7. Challenges:

Over-Reliance on AI: Risk of losing the personal touch which is crucial in a high-value, high-touch sell.

Misinterpretation: Incorrectly framed prompts can lead to irrelevant or misleading information.

Ethical Considerations: Ensuring the use of AI aligns with ethical standards and privacy regulations.

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The answers were greatly improved by clarity or as noted above by 'linguistic precision'. The icon images published above were also generated by ChatGPT 4. It took two iterations to ensure that the downloads were transparent .png images and it took a further two iterations to simplify these icons, by asking 'make it simpler' each time.

I think that the best practices most relevent (so far) are:

  1. Start with 3 things in mind: clarity; context; and, creativity.
  2. Linguistic precision for inputs. Clear language, shorter, be specific.
  3. Split complex tasks into simpler sub-tasks. Describe the steps.
  4. Create a role (persona) for identity: e.g. "As a Chief Revenue Officer ...".
  5. Apply parameters: specify the desired length of the output.
  6. Be iterative: add more context or key words over time.
  7. Ask AI for suggestions: "How do I ...".
  8. Nest prompts: two or more in new prompt (list n facts, write content on each).
  9. Creatre chain-of-thought prompt: e.g. audience, USPs, channels.
  10. Embrace AI non-deteminalism: same input, two or more different outputs.

More to follow ...