Google Gemini o1 Architecting Complex Chain of Thought in Gemini

One of my greatest inspirations growing up was Demis Hassabis. Inspired by his work, I entered college determined to study both Computer Science and Psychology, declaring a double major right during orientation—much to the surprise of the administration. Watching the progression of large language models (LLMs) over the past few years has been both exciting and, at times, frustrating, yet this field feels like home. My background in lab research on attention, cognition, and human thought has fueled a natural inclination to apply biological principles to machine learning and AI. Sharing these insights, especially with young learners, has been one of the most rewarding aspects of my journey.

This recent project—using Google’s Gemini model to replicate the reasoning capabilities of OpenAI’s o1 model—has taken me deeper into the very concepts that first ignited my passion for AI and cognitive science. Here’s how it all began, the hurdles along the way, and the potential for what’s next.

The Beginning: Mimicking Advanced Reasoning with Gemini

Initially, I wanted to see if I could coax Gemini, a Google Generative AI model, into adopting behaviors associated with Open AI's o1 models known for their unique chain of thought reasoning abilities. Gemini models are incredibly versatile, with high efficiency in token management, so this seemed like a fascinating challenge. With gemini-1.5-pro handling complex queries and tools like FAISS for vector search, I set out to create something that not only answers questions but “thinks” through them.

The First Bump: Optimizing the Model Selection

One challenge was dynamically choosing the right model from the Gemini lineup, as each offers unique processing power and token usage limits. To streamline this, I built a scoring system based on past model performance, success rates, and average token usage. This helps the system select the best-suited model for each query based on the query’s complexity and the model's purpose. The performance data logged from each interaction allows the system to “learn” which configurations yield the most efficient, high-quality answers.

Building “Memory” and Contextual Retrieval

Building a kind of “memory” was key to delivering relevant and coherent answers over multiple queries. By storing previous interactions and synthesizing example queries, I could guide Gemini to recall past responses. Using FAISS with embeddings generated by SentenceTransformers, I created a knowledge base that lets the model “remember” and reference prior interactions. This creates a cohesive, human-like interaction style, where responses grow more informed as the conversation progresses.

Emulating o1’s Reasoning Depth with Meta-Prompts

o1 uses structured “chain-of-thought” prompts, helping it break down complex queries into simpler parts. To achieve something similar, I developed a system of meta-prompts that help guide the AI through the reasoning process. These meta-prompts break down the overall strategy for answering queries, suggest relevant knowledge domains, propose angles to explore, and provide guidance on structuring responses. By steering the AI to follow these structured steps, Gemini begins to emulate the thoughtfulness and logical flow of the o1 model.

The Self-Improving Cycle: Prompt Optimization

An interesting part of the project was creating an optimization loop based on recurring query patterns. After accumulating enough interactions, the system applies KMeans clustering to identify query clusters, which helps to refine and optimize prompts. This enables the model to identify and address recurring query types more efficiently, while also improving the structure and focus of responses. For each query cluster, the system detects common patterns and suggests optimized prompt templates, refining responses over time and creating a more personalized, insightful experience.

Architecting an AI System for Continuous Self-Improvement

We’ve built an AI model that leverages Google Gemini’s generative capabilities alongside structured feedback loops. This iterative process enables the AI to refine its responses based on each interaction, moving toward more precise, insightful, and actionable outputs with every use.

System Structure: Three Pillars of Self-Improvement

The AI’s self-enhancement mechanism is supported by three essential components:

1. Initial Query Analysis: The system first evaluates the incoming query’s complexity, determining the best response strategy based on the user's needs.

2. Self-Critique and Iterative Refinement: Once the AI produces a response, it performs a critique assessing key aspects like clarity, factuality, and completeness. This critique directly informs an improved version of the initial answer.

3. Synthetic Data Generation for Knowledge Expansion: The system then transforms these interactions into new, synthesized query-response pairs, enriching the knowledge base with high-quality examples that boost performance for similar questions in the future.

Case in Point: Responding to Educational Needs for Twice-Exceptional Students

Let’s examine how these components work together in a real-world application, specifically in answering the complex question, “How can we better teach twice-exceptional kids?” (students who possess exceptional abilities but also face learning challenges).

1. Generating the Initial Response: The AI crafted a comprehensive response that outlined key challenges for twice-exceptional (2e) students, such as the “masking effect” (where strengths obscure learning challenges) and the emotional hurdles they often face. It recommended differentiated instruction techniques, like content modification and assistive technology, and underscored the importance of collaborative support from educators and parents.

2. Self-Critique and Enhanced Refinement: The AI then assessed its response, pinpointing strengths and potential enhancements:

- Strengths Identified: The response provided clear, organized content that addressed core needs across academic, social, and emotional areas.

- Refinements Needed: The critique highlighted areas where depth could be improved, such as including real-world examples and practical strategies. It also flagged opportunities to discuss systemic issues, like resource limitations and teacher training.

3. Refining the Response: Using these insights, the AI produced a refined answer, adding tangible examples—like assistive technology for reading and flexible grouping strategies—and a specific call to action for stakeholders. The enhanced response included this streamlined summary:

- “To effectively teach twice-exceptional kids, educators must create inclusive environments that offer individualized support for learning differences while fostering opportunities to challenge and nurture their exceptional talents.”

4. Expanding Knowledge Through Synthetic Data: Finally, the system used this interaction to generate three new, high-quality query-response pairs. These enriched its knowledge base, ensuring that future queries about twice-exceptional education would receive similarly informed and consistent responses.

Demonstrating Long-Term Impact

By combining structured self-critique, data generation, and continual refinement, this AI system transforms each interaction into a building block for growth. The model doesn’t merely answer questions; it iteratively learns, improves, and delivers value on an expanding scale.

This approach represents a new generation of adaptive learning systems—AI that genuinely learns from experience, offering increasingly personalized insights and becoming a dynamic partner in educational support, customer service, and beyond. Be on the lookout soon to see how I am going to use the synthetic data I collected!