Lesson 2: Inside Gemini - How it Thinks
The Building Blocks: Neural Networks and Transformers
At its core, Gemini is built on advanced neural networks. Specifically, Gemini's neural network is built on a type of architecture called a transformer. While the details can get highly technical, the core concepts are accessible to anyone, and understanding them can help you use Gemini to its fullest potential.
Learning Outcomes
By the end of this lesson, you will:
- Understand the fundamental architecture that powers Gemini
- Learn how Gemini processes different types of information (text, images, etc.)
- Discover how Gemini's training and learning process works
- Recognize how Gemini reasons through problems and generates responses
- Identify Gemini's key technological advantages and limitations
Neural Networks: The Digital Brain
Neural networks are computing systems loosely inspired by the human brain. They consist of interconnected nodes (artificial neurons) organized in layers:
- Input layer: Receives the initial data (your question or prompt)
- Hidden layers: Where the actual processing happens
- Output layer: Produces the response
Information flows through these layers, with each connection having a "weight" that strengthens or weakens as the system learns.
The Transformer Revolution
While neural networks have existed for decades, the transformer architecture (introduced by Google researchers in 2017) revolutionized AI by solving a critical problem: understanding context and relationships in sequences of information.
The key innovation is a mechanism called self-attention, which allows the model to weigh the importance of different words or elements in relation to each other. This is critical for understanding language, where meaning depends on relationships between words.
For example, in the sentence "The trophy wouldn't fit in the suitcase because it was too big," what does "it" refer to? A transformer can determine that "it" most likely refers to "the trophy" by examining the relationships between all words in the sentence.
This ability to track relationships between elements is what makes transformers exceptionally good at:
- Understanding natural language
- Maintaining coherent conversations
- Following complex instructions
- Connecting ideas across long contexts
How Gemini Processes Information
When you interact with Gemini, a sophisticated sequence of processes occurs behind the scenes:
From Human Input to AI Understanding
- Tokenization: Your input is broken down into "tokens" (words, parts of words, or characters)
- Embedding: These tokens are converted into numerical vectors (essentially coordinates in a high-dimensional space)
- Processing: The transformer processes these vectors through its many layers
- Decoding: The numerical output is converted back into human-readable text
The Multimodal Magic
If you've read Lesson 2 of our Getting Started With ChatGPT module, this discussion about neural networks, tokens, and transformers will sound familiar; however, Gemini has one feature that sets it apart from other LLMs.
What makes Gemini special is its ability to process multiple types of information (text, images, audio, and more) in a unified way. This is achieved through specialized components:
- Text encoder: Processes written language
- Image encoder: Analyzes visual information
- Joint embedding space: Where different types of information are mapped to a common representation
- Cross-modal attention: Mechanisms that relate elements across different modalities
For example, when you show Gemini an image with a question about it, the system can analyze the visual elements, understand the text query, and create connections between them to generate a relevant response.
The Learning Process: How Gemini Gets So Smart
Gemini's capabilities come from extensive training on diverse data. This process happens in several phases:
Pre-training: Building the Foundation
Before Gemini ever talks to you, it undergoes massive pre-training on a diverse corpus of data:
- Trillions of words from books, articles, websites
- Millions of images with descriptions
- Code repositories and documentation
- Videos with transcripts and descriptions
This training teaches the model statistical patterns: which words tend to follow others, how language relates to images, how concepts connect across domains, and so on.
Fine-tuning: Refining the Skills
After pre-training, Gemini undergoes fine-tuning for specific capabilities and behaviors:
- Following instructions precisely
- Maintaining helpful, accurate responses
- Specialized skills like coding or creative writing
This phase uses smaller, more curated datasets with specific examples of the desired behavior.
RLHF: Learning from Human Feedback
A critical step in Gemini's development is Reinforcement Learning from Human Feedback (RLHF):
- The model generates multiple potential responses to a prompt
- Human evaluators rate these responses on helpfulness, accuracy, safety, etc.
- This feedback trains a "reward model" that predicts human preferences
- The system is optimized to maximize this reward, aligning it with human values and expectations
No Learning During Your Conversation
As an LLM, Gemini is impressive because of how similar it feels to interacting with a human. But there is an important distinction: Unlike humans, Gemini doesn't learn or permanently update its knowledge during your conversation. While it maintains context within a session, it doesn't store your interactions to modify its fundamental capabilities. Each new conversation starts with the same baseline knowledge.
How Gemini Reasons and Generates Responses
Gemini's most impressive capability is its ability to reason: to work through problems step-by-step rather than simply pattern-matching.
Chain-of-Thought Reasoning
When faced with a complex problem, Gemini doesn't just jump to an answer. Instead, it uses chain-of-thought reasoning, breaking down problems into logical steps.
For example, when solving a math problem, it might:
- Identify the variables and relationships
- Recall relevant formulas or principles
- Apply appropriate operations in sequence
- Check the answer for consistency
The Generation Process: Predicting Word by Word
When Gemini creates a response, it doesn't retrieve pre-written answers from a database. Instead, it generates text word by word:
- The model considers your prompt and the conversation context
- It predicts the most appropriate first word
- That word is added to the context
- It predicts the next word given all previous words
- This process repeats until the response is complete
This generative approach is why Gemini can produce novel, contextually appropriate responses to prompts it has never seen before. It's a little similar to the autocomplete feature on most smartphones; it should come as no surprise that Gemini is used for autocomplete features on other Google services like Gmail and Google Docs.
Mixture-of-Experts: Gemini's Specialized Brain Regions
An architectural innovation in Gemini (especially version 1.5 and beyond) is the Mixture-of-Experts (MoE) design:
Instead of routing all tasks through the same neural pathways, Gemini activates different "expert" subnetworks depending on the type of task.
Some experts specialize in language understanding, others excel at visual processing, others focus on mathematical reasoning, and so on.
This approach is more efficient and effective than forcing a single network to handle all tasks, and it's similar to how the human brain has specialized regions for different functions.
Gemini's Memory Systems: How Context Works
Gemini's ability to maintain coherent conversations depends on how it manages information through different types of "memory":
Context Window: The Working Memory
Gemini's "context window" refers to how much information it can consider at once. This is essentially its working memory:
- Gemini 1.0: ~32,000 tokens (roughly 25,000 words)
- Gemini 1.5: Up to 1,000,000 tokens (equivalent to a 700-page book)
Everything within this window, such as your original question, previous exchanges, and any documents you've shared, is available for Gemini to reference when generating its next response.
How Context Influences Responses
The expanded context window enables sophisticated behaviors:
- Reference resolution: Understanding pronouns and references to earlier content
- Consistency: Maintaining a coherent conversation thread
- Document analysis: Processing and reasoning about entire documents
- Cross-referencing: Connecting information from different parts of a long exchange
For instance, if you ask "When was it founded?" after discussing Harvard University, Gemini knows "it" refers to Harvard because that information is in its context window.
Session vs. Persistent Memory
It's important to understand the difference between types of AI memory:
- Session memory: Information retained only during your current conversation
- Persistent memory: Information the AI stores across sessions (implemented in Gemini through features like "Saved Info")
By default, each new conversation with Gemini starts fresh, like we mentioned earlier; it doesn't automatically remember previous sessions unless you explicitly use memory features.
Multimodal Understanding: Processing Beyond Text
A key advancement in Gemini is its ability to process multiple types of information in an integrated way. This is what we referred to as "multimodal" in Lesson 1:
Visual Processing Capabilities
When you share an image with Gemini, specialized neural networks analyze it on multiple levels:
- Object recognition: Identifying people, animals, objects, text
- Scene understanding: Grasping the overall context and setting
- Spatial relationships: Noting how elements relate to each other
- Text in images: Reading and interpreting any visible text
- Visual attributes: Processing colors, styles, compositions
Connecting Vision and Language
The true power comes from Gemini's ability to connect visual understanding with language processing:
- Answer questions about visual content
- Describe images in detail
- Relate visual elements to textual concepts
- Follow instructions that reference visual elements
For example, you could show Gemini a complex diagram and ask specific questions about its components, and Gemini can "see" what you're referring to and provide relevant explanations.
Beyond Images: Other Modalities
While Gemini's capabilities continue to expand, it can also work with:
- Code: Understanding programming languages, debugging, and generating functional code
- Audio: Integration with speech recognition (in some versions)
- Structured data: Processing tables, graphs, and other data formats
Technological Advantages and Limitations
Why bother learning about how Gemini works under the hood? Well, understanding both Gemini's strengths and limitations helps you use it more effectively.
Key Technological Advantages
- Unified multimodal architecture Unlike systems that use separate models for different data types, Gemini's unified architecture enables seamless multimodal understanding.
- Extensive context window The million-token context window in Gemini 1.5 allows for analysis of entire documents or extended conversations without losing track of information.
- Mixture-of-Experts efficiency The MoE architecture enables more efficient processing by activating only relevant "expert" neural pathways for each task.
- Advanced reasoning capabilities Chain-of-thought processes allow for step-by-step problem solving rather than simple pattern recognition.
Understanding Limitations
- "Hallucinations" and confident errors: Because Gemini generates responses based on statistical patterns rather than accessing a verified database of facts, it can occasionally produce incorrect information with high confidence, which is a phenomenon often called "hallucination."
- Training data boundaries Gemini's knowledge is limited to the data it was trained on, with a specific cutoff date. It doesn't have real-time access to the internet unless specifically integrated with search tools.
- Contextual misunderstandings While Gemini's context management is advanced, it can still misinterpret ambiguous references or complex contextual cues.
- Reasoning limitations Despite impressive capabilities, Gemini's reasoning still falls short of human-level understanding in some domains, particularly those requiring commonsense knowledge or cultural nuance.
Try It Yourself: Observing Gemini's Thinking
To better understand how Gemini processes information, try this experiment:
- Open a conversation with Gemini and ask it to solve a moderately complex math problem like: "A store is having a 30% off sale. If an item normally costs $85, what is the sale price? And how much money will I save?"
- Then add: "Please show your step-by-step reasoning process."
- Observe how Gemini breaks down the problem:
- Identifying the relevant information
- Applying the appropriate formulas
- Working through calculations systematically
- Presenting the final answer
This exercise demonstrates chain-of-thought reasoning in action, giving you a window into how Gemini approaches problem-solving.
Key Learnings & Takeaways
Let's consolidate the most important concepts from this chapter:
- Foundation in transformers: Gemini is built on transformer neural networks that use self-attention mechanisms to understand relationships between elements in data.
- Sophisticated learning process: Before you ever interact with it, Gemini undergoes extensive pre-training, fine-tuning, and reinforcement learning from human feedback.
- Generative creation: Rather than retrieving pre-written answers, Gemini generates responses word by word based on patterns learned during training.
- Chain-of-thought reasoning: For complex problems, Gemini can break down tasks into logical steps, similar to human problem-solving.
- Multimodal integration: Gemini processes text, images, and other input types through a unified architecture that allows cross-modal understanding.
- Context management: Through its extensive context window, Gemini maintains awareness of previous exchanges and provided information throughout a conversation.
- MoE efficiency: The Mixture-of-Experts architecture routes different tasks to specialized neural subnetworks, improving both performance and efficiency.
What's Next?
Now that you understand how Gemini works internally, the next chapter will guide you through setting up and accessing Gemini for your own use. You'll learn how to:
- Create your Google Cloud project
- Enable necessary APIs
- Configure your environment
- Run your first Gemini prompts
This practical foundation will prepare you to apply the technological concepts you've learned in real-world scenarios.
