Dear Negotiation Explorer,

Welcome to Week 7 of our NegoAI series.

In this week's video, I demonstrate how to use Deepak, our AI negotiation preparation assistant, to analyze scenarios efficiently and develop strategic insights.

Developing Intelligent Assistance

Designing Deepak requires careful construction of system instructions that guide its behavior. These instructions consist of distinct components that create a robust foundation:

• Role defines the assistant’s expertise and the perspective it adopts.

• Context provides background to help the system understand the setting.

• Instructions detail the tasks Deepak must perform and the expected outputs.

• Extrapolation encourages going beyond standard frameworks to generate creative strategies.

• Criteria set standards for evaluating the quality of responses.

• Examples offer meta-level reasoning that guides adaptive outputs.

• Continuous Improvement ensures the assistant evolves based on feedback and ongoing learning.

Together, these building blocks enable Deepak to perform in a consistent and insightful manner, positioning it as a strategic partner in negotiation preparation.

Key Features of Deepak

Once operational, Deepak enhances negotiation readiness by:

1. Breaking down complex scenarios into executive summaries, including estimates of the Zone of Possible Agreement (ZOPA), potential biases, and key leverage points.

2. Providing strategic options tailored to different counterpart motivations, from phased pricing to bundled offers, helping you anticipate and prepare for a range of responses.

3. Generating creative opportunities that expand the negotiation “pie,” encouraging integrative approaches that create value for all parties.

4. Recommending trust-building measures such as joint press releases or onsite inspections to strengthen relationships and reduce risks.

Technical Implementation Notes

For those interested in understanding how Deepak operates under the hood, here are some key technical considerations:

• Model and Temperature Settings: Deepak employs the latest ChatGPT-4.1 model. The temperature is set around 0.6, balancing creativity with consistency to allow innovative yet reliable negotiation insights.

• System Instruction Framework: Deepak's effectiveness stems from a modular system of carefully designed instruction layers that guide its reasoning and output generation:

  • Role Definition: Assigns Deepak a clear mandate, such as acting as a negotiation strategist and advisor, which focuses its responses within a purposeful negotiation context.

  • Knowledge Contextualization: Dynamically shapes the assistant’s understanding by embedding relevant background—whether industry specifics, company policies, or negotiation history—ensuring outputs are appropriately grounded.

  • Operational Directives: Specifies how Deepak processes information, prioritizes negotiation elements, and sequences its analysis, enforcing a consistent workflow that balances breadth and depth.

  • Innovative Reasoning Encouragement: Incorporates heuristic triggers that prompt Deepak to synthesize concepts across negotiation paradigms, encouraging the generation of unique strategic options unrivaled by scripted responses.

  • Quality Assurance Criteria: Implements built-in checks for logical coherence, relevance, completeness, and practical applicability, ensuring recommendations are sound and actionable.

  • Adaptive Example Integration: Utilizes meta-examples that exemplify high-level reasoning without constraining flexibility, allowing Deepak to tailor outputs creatively to diverse scenarios.

  • Continuous Feedback Loop: Embeds mechanisms to assimilate user feedback, calibrate judgment, and refine heuristics over time, enabling sustained improvement and contextual sensitivity.

• Processing Pipeline: The assistant integrates internal "thinking time" allowing for multi-step reasoning before delivering final analyses, improving output quality.

• Context Window and Token Management: Deepak operates with a substantial context window—currently sized at half the maximum tokens ChatGPT-4.1 supports—ensuring it handles complex, detailed negotiation scenarios without truncating critical data.

• Customization and Scalability: While designed with broad context, Deepak’s instructions can be customized for departmental or industry-specific negotiation needs, making it adaptable and scalable.

• Validation and Evaluation: Continuous testing against known roleplays and scenarios ensures accuracy and reliability, with performance benchmarks reviewed regularly to maintain consistent quality.

This technical foundation allows Deepak to provide structured, strategic negotiation support that is both innovative and aligned with real-world complexities.

This Week’s Exercise

Spend 20 minutes identifying a current or upcoming negotiation where an assistant like Deepak could improve your outcomes. Consider:

• Which parts of your preparation would benefit from fast, organized analysis?

• Where could anticipating multiple counterpart profiles or creative options change your approach?

• How might systematically integrating trust-building strategies move the negotiation forward?

Preview of Next week

Next week, we will integrate behavioral AI agents with Deepak, enabling even more precise strategies by factoring in personality and communication styles. This will further enhance preparation quality and response agility.