Agentic AI Assistant & Workflow
Creating an AI involves several steps, each requiring different skills and knowledge. Here's a simplified overview:
1. Define the Problem:
Identify the specific task or problem you want the AI to solve.Clearly define the desired outcome and success metrics.
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2. Choose the AI Approach:
Machine Learning: Train an AI model on existing data to learn patterns and make predictions.
Deep Learning: Use artificial neural networks to learn complex patterns from large amounts of data.Natural Language
Processing (NLP): Enable AI to understand and process human language.
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Computer Vision: Allow AI to interpret and analyze visual information.
3. Data Acquisition and Preparation:
Gather relevant data for training the AI model.Clean and pre-process the data to ensure accuracy and consistency.
4. Model Development:
Choose the appropriate algorithms and tools for building the AI model.Train the model on the prepared data and fine-tune its parameters.
5. Evaluation and Testing:
Evaluate the model's performance using various metrics and test data.Identify and address any biases or errors in the model.
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6. Deployment and Monitoring:
Integrate the trained AI model into your application or system.Monitor the model's performance and make adjustments as needed.
Additional Considerations:
Ethical Considerations: Ensure your AI is developed and used responsibly, avoiding bias and discrimination.
Security and Privacy: Protect user data and ensure the AI system is secure from cyberattacks.
Explainability and Transparency: Understand how the AI model makes decisions and be able to explain its reasoning.
With Large Language Models (LLMs), we can also do:
Process natural language: LLM's can understand human language to some degree, breaking it down into concepts, keywords, syntax, etc.
Generate language: Many LLM's like Chatbots are designed to hold Conversations by generating natural language responses to user queries or statements.
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Answer questions: By analyzing language and querying internal databases of information, LLM's can attempt to answer factual questions users pose to them.
Summarize text: LLM's have capabilities to take longer passages of text and automatically generate summaries preserving the key points.
Translate between languages: Models like Google Translate utilize LLM techniques for machine translation of text between different human languages.
Classify text: LLM's can performtasks like sentiment analysis, topic classification,Named entity recognition to classify aspects of text.
Make predictions: With access to large datasets, LLM's can be trained to predict future outcomes or provide recommendations based on prior data patterns.
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Generate text: More advanced LLM's allow for text generation capabilities like completing sentences, writing stories, poems or other multi-paragraph passages.
Provide information: LLM's aggregate external knowledge sources to retrieve definitions, facts, biographies and other information to share with users.