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Fundamental AI Concepts

To make the most of NeoLang, it's important to understand some fundamental artificial intelligence concepts that underpin its operation. This section explains the main concepts in an accessible way, even for those with no prior AI experience.

Large Language Models (LLMs)​

Large Language Models (LLMs) are artificial intelligence models trained on vast text datasets to understand and generate natural language. They work by predicting the next word in a sequence, based on the provided context.

How LLMs work​

  1. Pre-training: The model is trained on large volumes of text to learn linguistic patterns and general knowledge
  2. Fine-tuning: The model is refined on domain-specific data (in NeoLang's case, financial data)
  3. Inference: The model processes an input (prompt) and generates an output based on its training

Limitations of LLMs​

  • Hallucinations: They can confidently generate incorrect information
  • Bias: They may reflect biases present in the training data
  • Complex reasoning: They may struggle with certain types of reasoning
  • Recency: Knowledge limited to data up to the training cutoff date

NeoLang implements various techniques to mitigate these limitations, especially in the financial context.

RAG Architecture (Retrieval-Augmented Generation)​

NeoLang uses the RAG (Retrieval-Augmented Generation) architecture to improve the accuracy and relevance of responses.

graph LR
    A[User Query] --> B[Retrieval System]
    A --> C[Response Generator]
    B --> D[Knowledge Base]
    D --> E[Relevant Documents]
    E --> C
    C --> F[Final Response]

RAG Components​

  1. Retrieval System: Identifies relevant information in a knowledge base
  2. Knowledge Base: Repository of financial documents, regulations, and specific data
  3. Response Generator: Combines the original query with retrieved information to generate an accurate response

Advantages of RAG in the financial context​

  • Updated information: Access to recent financial data
  • Accuracy: Direct reference to reliable sources
  • Transparency: Ability to cite specific sources
  • Compliance: Assurance that responses align with current regulations

Virtual Experts​

In NeoLang, virtual experts are specialized components designed for specific financial domains. Each expert has:

  • Specialized knowledge: Training in a specific financial area
  • Context: Background information about the domain
  • Tools: Access to specific tools for their area of expertise
  • Instructions: Guidelines on how to respond to queries in their domain

Types of experts in NeoLang​

  • Financial Analyst: Specialized in financial statement analysis
  • Investment Advisor: Focused on investment strategies
  • Compliance Specialist: Specialized in financial regulations
  • Credit Analyst: Focused on credit risk analysis
  • Financial Planner: Specialized in personal financial planning

Tools​

Tools are components that allow NeoLang to perform specific actions beyond text generation. They significantly expand the system's capabilities.

How tools work​

  1. The model identifies when a tool is needed
  2. The model generates the necessary parameters for the tool
  3. The tool is executed with these parameters
  4. The result is incorporated into the final response

Tools available in NeoLang​

  • Financial Calculator: Performs complex financial calculations
  • Document Analyzer: Extracts information from financial documents
  • Regulation Finder: Searches for relevant financial regulations
  • Data Visualizer: Generates financial charts and visualizations
  • Product Comparator: Compares different financial products

Orchestration​

Orchestration is the process of coordinating multiple AI components to solve a complex task. In NeoLang, orchestration manages:

  • Routing: Directing queries to appropriate experts
  • Task decomposition: Breaking complex problems into subtasks
  • Result aggregation: Combining multiple responses into a coherent response
  • Verification: Validating the accuracy and compliance of responses

NeoLang orchestration pattern​

sequenceDiagram
    participant User
    participant Router
    participant Expert1 as Expert A
    participant Expert2 as Expert B
    participant Tool
    participant Orchestrator
    
    User->>Router: Sends query
    Router->>Router: Analyzes intent
    Router->>Expert1: Directs to Expert A
    Expert1->>Tool: Requests calculation
    Tool-->>Expert1: Returns result
    Expert1->>Orchestrator: Sends partial response
    Router->>Expert2: Requests verification
    Expert2->>Orchestrator: Sends validation
    Orchestrator->>User: Delivers final response

Guardrails​

Guardrails are mechanisms that ensure NeoLang operates within safe and appropriate boundaries, especially important in the financial sector.

Types of implemented guardrails​

  • Regulatory compliance: Ensure responses comply with financial regulations
  • Factual accuracy: Verify the accuracy of financial information
  • Safety: Block harmful or unauthorized content
  • Privacy: Protect sensitive personal and financial information
  • Transparency: Clearly indicate when a response is an opinion vs. established fact

Evaluation and Metrics​

NeoLang uses a comprehensive set of metrics to evaluate the quality of responses:

Quantitative metrics​

  • Factual accuracy: Percentage of correct financial facts
  • Compliance: Rate of adherence to applicable regulations
  • Usefulness: Assessment of the relevance of the response to the query
  • Latency: Response time for financial queries

Qualitative evaluation​

  • Clarity: Ease of understanding the response
  • Completeness: Comprehensiveness of the response in relation to the query
  • Personalization: Adaptation to the user's level of knowledge
  • Contextualization: Consideration of the specific context of the query

Next steps​

Now that you understand the fundamental concepts behind NeoLang, explore: