Gemini AI Cheatsheet

Cheatsheet Content

### Gemini: An Overview Gemini is a family of multimodal large language models (LLMs) developed by Google AI. Designed for flexibility and performance across various tasks and modalities. #### Key Features - **Multimodality:** Understands and operates across text, code, audio, image, and video. - **Scalability:** Available in different sizes (Nano, Pro, Ultra) for diverse applications. - **Advanced Reasoning:** Capable of complex problem-solving, planning, and understanding nuances. - **Tool Integration:** Designed to interact with external tools and APIs. #### Versions - **Gemini Nano:** Smallest, most efficient model for on-device applications (e.g., smartphones). - **Gemini Pro:** Mid-sized model, optimized for a wide range of tasks and scalable cloud deployment. - **Gemini Ultra:** Largest, most capable model for highly complex tasks and cutting-edge performance. ### Gemini Nano Optimized for efficiency, ideal for on-device AI. #### Use Cases - **Summarization:** Quickly summarize text on a mobile device. - **Contextual Smart Reply:** Generate relevant responses in messaging apps. - **Image Analysis:** Basic image understanding without cloud latency. - **Offline Capabilities:** Performs tasks without an internet connection. #### Key Characteristics - **Low Latency:** Designed for quick responses. - **Resource-Efficient:** Minimal power and memory consumption. - **Privacy-Focused:** Data processed locally, enhancing user privacy. - **Integration:** Primarily through Android AI Core and similar on-device SDKs. ### Gemini Pro A versatile model for general-purpose applications, balancing performance and cost. #### Use Cases - **Content Generation:** Draft emails, articles, creative text formats. - **Code Generation & Explanation:** Write code snippets, debug, explain complex functions. - **Data Analysis:** Extract insights from structured and unstructured data. - **Chatbots & Virtual Assistants:** Power conversational AI experiences. - **Multimodal Input:** Process and generate content based on combinations of text, images, and audio (via API). #### Key Characteristics - **Scalable:** Deployed via Google Cloud Vertex AI and Google AI Studio. - **Balanced Performance:** Strong across many benchmarks. - **API Access:** Primary mode of interaction for developers. - **Modality Support:** Native multimodal understanding. ### Gemini Ultra The most powerful and capable Gemini model, designed for highly complex tasks. #### Use Cases - **Advanced Scientific Research:** Analyze complex datasets, assist in hypothesis generation. - **Complex Reasoning:** Solve intricate problems requiring deep understanding and planning. - **Enterprise Solutions:** High-stakes applications requiring maximum accuracy and capability. - **Cutting-Edge AI Development:** For developers pushing the boundaries of AI. - **Multimodal Synthesis:** Generate highly detailed and coherent outputs from diverse inputs. #### Key Characteristics - **State-of-the-Art Performance:** Outperforms other models on many benchmarks. - **Highest Latency/Cost:** Due to its complexity, it has higher resource requirements. - **Deep Multimodal Understanding:** Excels at interpreting subtle cues across modalities. - **Limited Access:** Typically available to select users and enterprises first. ### Common Concepts & API Interactions (Gemini Pro/Ultra) #### Prompting Techniques - **Few-shot Prompting:** Provide examples to guide the model's output. - **Chain-of-Thought Prompting:** Ask the model to explain its reasoning step-by-step. - **Role-Playing:** Assign a persona to the model for specific interactions. - **Multimodal Prompts:** Combine text with images, audio, or video for richer context. #### API Structure (Simplified) ```python import google.generativeai as genai # Configure API key genai.configure(api_key="YOUR_API_KEY") # Choose a model model = genai.GenerativeModel('gemini-pro') # or 'gemini-ultra' # Text-only prompt response = model.generate_content("Explain the concept of quantum entanglement.") print(response.text) # Multimodal prompt (e.g., text + image) # image_data = # Load your image # response = model.generate_content(["Describe this image:", image_data]) # print(response.text) # Chat interaction chat = model.start_chat(history=[]) response = chat.send_message("Hello, how are you?") print(response.text) response = chat.send_message("Can you tell me a fun fact?") print(response.text) ``` #### Important Considerations - **Safety Settings:** Gemini models have built-in safety filters. Adjust as needed for your application, but always prioritize ethical AI use. - **Token Limits:** Be mindful of input and output token limits for each model. - **Cost:** Usage costs vary by model and token consumption. - **Fine-tuning:** Possibility to fine-tune models for specific domain knowledge or tasks (availability varies).