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To contribute or comment please contact: Michael Meighu or Cedric Berger via Linked in
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Level:123456789
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Name:Using LLM in its Own InterfaceApp using a Foundational LLM via an APISimple RAGAdvanced RAG (including Graph RAG) RAG with Task Orientated Agents LLMs with Tool Integration for Calculations and ReasoningLLMs with Knowledge Graphs IntegrationInstructional Fine-Tuned Models Continual Learning and Adaptive systems
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Use cases:General Q&A
Basic Chat		General Q&A
Chat
Basic Integrations into 3rd party applications
Customer service bots		Document retrieval and summary
Domain-specific Q&A
Business specific chatbots		Complex document workflows (e.g. Medical reports)
Personalized context driven enterprise applications
Sophisticated business decision making support		Enterprise workflow automation
Complex multi-task execution
Data driven automation 		Executing real-time calculations
Automating code writing and debugging using integrated developer tools
Complex task workflows involving APIs and databases (e.g., data retrieval, processing, and reporting)
Performing actions based on reasoning over multiple data sources (e.g., auto-generation of reports, decision-making support)
Dynamic task execution		Context-aware conversational agents
Complex, multi-step decision-making processes based on structured and unstructured data
Enhanced search and retrieval systems that understand relationships between concepts
Personalized recommendations in regulated industries using contextualized data
Biomedical research support, including drug discovery and patient data synthesis
Data-driven risk analysis and regulatory compliance automation		Fine-tuned, domain specific models
Customizable chatbot experiences
High-accuracy decision support systems 		Adaptive systems that improve over time
Proactive task completion
Personalized AI-driven decision support in complex environments
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Business Impact Improves basic interatctions and general knowledge accessStreamlines basic interactions and integrates into existing systems.Increases efficiency by providing fast, relevant document retrieval.Automates complex document workflows, improving productivity.Automates enterprise-level tasks, reducing human intervention.Increased Decision-Making Power
Operational Efficiency
Advanced Customization
Scalability of Business Processes		Enhanced decision-making capabilities due to better contextual understanding and reasoning
Improved accuracy and efficiency in automating complex workflows
Knowledge continuity and collaboration across teams with reduced need for manual knowledge retrieval
Unlocking new insights from both structured and unstructured data, leading to operational efficiencies
Faster and more accurate answers in domains like drug discovery, clinical trials, and regulatory compliance		Custom solutions tailored to business, unlocking advanced automation.Ongoing system improvement, maximizes operational efficiency and adaptability.
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KPIsResponse time, User engagement System uptime, API call success rateDocument retrieval accuracy, User satisfactionAutomation coverage, Time saved in workflowsReduction in manual task completion, Process efficiencyTool Integration Efficiency
Accuracy of Computations/Reasoning
Reduction in Manual Task Completion
Time Saved in Complex Workflows		Knowledge retrieval accuracy
Task automation success rate (using LLM and Knowledge Graph integrations)
Reduction in time spent on complex queries or multi-step decision processes
Growth and adaptability of the Knowledge Graph over time (e.g., number of nodes, relationships)
Accuracy of entity linking and context-aware responses
Model’s ability to handle complex reasoning tasks (e.g., through Knowledge Graph-guided inference)		Model accuracy in fine-tuned tasks, Customization successModel drift detection
System adaptability
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Organizational ReadinessBasic NLP understandint, simple integration capability Basic API integration skills, LLM provider relationshipIn-house data management, Basic search integrationAdvanced search, NLP and document processing skillsComplex AI orchestration, Governance for task automationMature cross department collaboration
Advanced technical skills
Security protocols 		"Mature cross department collaboration
Advanced technical skills
Security protocols "		Expertise in fine-tuning models, Domain-specific knowledgeLifecycle management skills, Continual learning infrastructure
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Tasks:Text Classification
Token Classification
Table Question Answering
Question Answering
Zero Shot Classification
Translation
Summarization
Feature Extraction
Text Generation
Text2Text Generation
Fill Mask
Sentence Similarity		As before.As before but with additional:

Retrieval of specific documents or data based on prompts
Augmenting and LLM with domain knowledge
Handling structured and semi-structured queries		As before but with additional:

Retrieving and summarizing complex documents
Multi-turn Q&A within a specialised domain
Combining data retrieval with generative capabilities 		As before but with additional:

Agents that can execute tasks

Workflow orchestration
Automating routine tasks		As before but with additional:

Real time data processing
Advanced decision support
Automated workflow management
Custom task execution 		As before but with additional:

Semantic search and question answering using domain-specific knowledge
Automated report generation by synthesizing data from structured and unstructured sources
Complex reasoning tasks requiring cross-referencing between natural language inputs and structured knowledge
Entity recognition and disambiguation using Knowledge Graph context
Adaptive learning by updating the knowledge graph based on new data or model predictions		As before but with additional:

Fine-Tuning LLMs on proprietry data
Creating custom workflows based on fine tuned LLM models
Specialised conversations 		As before but with additional:

Continual model training and adaptation based on user interactions and new data
Implementing feedback loops to automatically refine model accuracy
Full-scale automation of end-to-end processes, with dynamic adaptability to changing conditions
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Architecture Needed:NoneAPI (Access to LLM services through simple API calls)
Basic server infrastructure for handling requests
Control layer		Orchestration Layer
Embedding Layer
Vector database
Control layer		Orchestration Layer
Advanced Vector Database
Embedding layer with more complex embeddings for nuanced data retrieval
Control layer 		Orchestration Layer
Advanced Vector Database
Embedding layer with more complex embeddings for nuanced data retrieval
Agent Management Layer
Automation systems integration (such as RPA)
Control layer 		Cloud and API Integration
Scalable Infrastructure
Data Pipelines
Microservices Architecture
Control layer 		Hybrid architecture combining LLMs (for natural language understanding) with Knowledge Graphs (for structured, contextual data)
Querying mechanisms to extract insights by linking entities, relationships, and concepts
Real-time integration of knowledge from external data sources (e.g., databases, APIs)
Feedback loops for continual learning and knowledge graph expansion
Control layer 		Fine-tuning infrastructure
Custom dataset integration with training pipeline
Continous monitoring and updating models
Control layer 		Advanced Continual Learning Framework (to support ongoing model retraining)
Feedback Loops and Monitoring Infrastructure (to capture and analyze performance data)
Real-time Data Processing Pipelines for adaptive decision-making
Highly scalable orchestration and agent layers
Control layer
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Complaince and Security Risks
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Mitigation
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Complexity:Low Moderate Moderate - HighHigh - Very HighVery high Very high - Extremely high Extremely high Extremely high Extremely high
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