LLM Integration & Generative AI

Production integration of large language models into real workflows. We implement fine-tuning, RAG systems, evaluation frameworks, and deployment strategies that deliver measurable value while managing risks.

LLM Capabilities

Fine-Tuning & Adaptation

LoRA and QLoRA-based fine-tuning for domain-specific tasks. HEC-funded research on parameter-efficient adaptation of Llama and Mistral models. We optimize for task performance while managing computational and data constraints.

RAG System Implementation

Retrieval-augmented generation with vector databases, semantic search, and context optimization. Build knowledge systems that ground LLM responses in your proprietary documentation and data sources.

Evaluation & Quality Assurance

Multi-metric evaluation including semantic similarity (BERT Score), traditional metrics (BLEU, ROUGE), LLM-as-judge assessment, and human review workflows. Our academic assignment generation research demonstrates systematic evaluation aligned with learning outcomes.

Multi-Agent Systems

Research and implementation of multi-agent LLM architectures. HEC-funded work exploring coordination strategies, task decomposition, and agent specialization for complex workflows.

Prompt Engineering & Optimization

Systematic prompt development, few-shot learning strategies, chain-of-thought prompting, and structured output formatting. We test prompt variations empirically and document what works for reproducible results.

Production Deployment

Model serving, API integration, cost optimization, monitoring, and safety guardrails. We handle latency requirements, fallback strategies, and integration with existing systems while managing token costs.

Research & Implementation Experience

  • HEC-funded research: LoRA fine-tuning and multi-agent LLM systems
  • LLM-assisted academic assignment generation with learning-outcome alignment (ICoDT2 2025 accepted poster)
  • Semantic and LLM-as-judge evaluation frameworks with human-in-the-loop review
  • Llama family and Mistral model experimentation and deployment
  • RAG implementation for document processing and knowledge systems

Frequently Asked Questions

What LLM integration services does Kortex8Labs provide?
We provide LLM fine-tuning with LoRA/QLoRA, RAG system implementation, prompt engineering, evaluation framework development, multi-agent systems, and production deployment of generative AI applications.
Which language models does Kortex8Labs work with?
We have research and production experience with Llama family models, Mistral, and open-source alternatives. Our HEC-funded research explores LoRA fine-tuning and multi-agent architectures for specialized tasks.
How does Kortex8Labs evaluate LLM outputs?
We use semantic similarity metrics (BERT Score), traditional metrics (BLEU, ROUGE), LLM-as-judge evaluation, human review workflows, and task-specific assessment. Our LLM assignment generation research includes multi-level evaluation comparing generated content against learning outcomes.
Can Kortex8Labs implement RAG systems?
Yes. We implement retrieval-augmented generation systems with vector databases, embedding models, chunk optimization, and retrieval strategies. Experience includes knowledge systems and document processing applications.
How do you ensure LLM safety and reliability?
We implement output validation, content filtering, confidence scoring, human review workflows where appropriate, and fallback strategies. Evaluation includes testing edge cases and adversarial inputs relevant to your domain.

Discuss Your LLM Project

Describe the workflow where language models could add value. We'll assess technical feasibility, recommend an approach, and identify evaluation criteria.

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