I get many questions about the radically different LLM technology that I started to develop 2 years ago. Initially designed to retrieve information that I could no longer find on the Internet, not with search, OpenAI, Gemini, Perplexity or any other platform, it evolved to become the ideal solution for professional enterprise users. Now agentic and multimodal, automating business tasks at scale with lightning speed, consistently delivering real ROI, bypassing the costs associated to training and GPU with zero weight and explainable AI, tested and developed for Fortune 100 company.

So, what is behind the scenes, how different is it compared to LLM 1.0 (GPT and the likes), how can it be hallucination-free, what makes it a game changer, how did it eliminate prompt engineering, how does it handle knowledge graphs without neural networks, and what are the other benefits?

In a nutshell, the performance is due to building a robust architecture from the ground up and at every step, offering far more than a prompt box, relying on home-made technology rather than faulty Python libraries, and designed by enterprise and tech visionaries for enterprise users.

Contextual smart crawling to retrieve underlying taxonomies, augmented taxonomies, long contextual multi-tokens, real-time fine-tunning, increased security, LLM router with specialized sub-LLMs, an in-memory database architecture of its own to efficiently handle sparsity in keyword associations, contextual backend tables, agents built on the backend, mapping between prompt and corpus keywords, customized PMI rather than cosine similarity, variable-length embeddings, and the scoring engine (the new “PageRank” of LLMs) returning results along with the relevancy scores, are but a few of the differentiators.

Keep in mind that trained models (LLM 1.0) are trained to predict the next tokens or to guess missing tokens but not trained to accomplish the tasks they are supposed to do. The training comes with a big price tag: billions of parameters and a lot of GPU. The client ends up paying the bill. Yet the performance comes from all the heavy machinery around the neural networks, not from the neural networks themselves. And model evaluation fails to assess exhaustivity, conciseness, depth, and many other aspects.

All the details with case studies, datasets, and Python code are in my new book, here, with links to GitHub. In this article, I share section 10.3, contrasting LLM 2.0, to LLM 1.0. Several research papers are available here.

LLM 2.0 versus 1.0

I broke down the differentiations into 5 main categories. Due to the innovative architecture and next gen features, xLLM constitutes a milestone in LLM development, moving away from the deep neural network (DNN) machinery and its expensive black-box training and GPU reliance, while delivering more accurate results to professional users, especially for enterprise applications. Here the abbreviation KG stands for knowledge graph.

1. Foundations

• LLM 2.0. Solid foundations to design robust back-end architecture from the ground up, retrieve and leverage the knowledge graph from the corpus (smart crawling). Hallucination-free, no need for prompt engineering. Zero weight. Suggested alternate prompts based on embeddings.
• LLM 1.0. Poor back-end architecture. Knowledge graph built on top (top-down rather than bottom-up approach). Needs prompt engineering and billions of weights. Yet, the success depends more on auxiliary subsystems rather than on the core DNN engine.

2. Knowledge graph, context

• LLM 2.0. Few tokens:  “real estate San Francisco” is 2 tokens. Contextual chunks, KG and contextual tokens with non-adjacent words, sorted n-grams, customizable PMI metric for keyword associations, variable-length embeddings, in-memory nested hashes for KG backend DB.
• LLM 1.0. Tons of tiny tokens. Fixed-size chunks and embeddings are common. Vector databases, dot product and cosine similarity instead of PMI. Reliance on faulty Python libraries for NLP. One type of token: no KG or contextual tokens.

3. Relevancy scores, exhaustivity

• LLM 2.0. Focus on conciseness, accuracy, depth and exhaustivity in prompt results. Normalized relevancy scores displayed to the user, to warn him of potential poor answers when corpus has gaps. Augmentation and use of synonyms to map prompt keywords to tokens in backend tables, to boost exhaustivity and minimize gaps. Prompt results (front-end) distillation.
• LLM 1.0. Focus on lengthy English prose aimed at novices, in prompt results. Evaluation metrics do not measure exhaustivity or depth. No relevancy scores shown to the user or used in model evaluation. No mechanism to reduce gaps other than augmentation. Back-end distillation needed to fix poor corpus or oversized token lists.

4. Specialized sub-LLMs

• LLM 2.0. Specialized sub-LLMs with LLM router. User can choose categories, agents (built in the backend), negative keywords, or retrieve content based on recency. Or fine tune front-end intuitive parameter in real-time, with debugging option. Process prompts in bulk. Fine tune back-end parameters. Popular user-chosen parameters used for self-tuning, to generate default parameter sets. No training needed. Parameters local to sub-LLM, or global.
• LLM 1.0. User interface limited to basic search box, doing one prompt at a time. No real-time fine-tuning, little if any customization available: the system guesses user intents (the agents). Fine-tuning for developers only, may require re-training the full model (costly), and it is based on black-box parameters rather than explainable AI. Needs regular training as new keywords show up and the model is not trained on them.

5. Deep retrieval, multi-index chunking

• LLM 2.0. Use of multi-index and deep retrieval techniques (e.g. for PDFs). Highly secure (local, authorized users). Can connect to other LLMs or call home-made apps (NoGAN synthesizer, LLM for clustering, cataloging, auto-indexing or predictions). Taxonomy and KG augmentation. Pre-made template answers with keyword plugging to cover many prompts.
• LLM 1.0. Single index. Proprietary and standard libraries may miss some tables, graphs and other elements in PDFs: shallow retrieval. No KG Augmentation. Data leakage; security and liability issues (hallucinations). Long answers favored over conciseness and structured output.