10 Must-Read Articles and Books About Next-Gen AI in 2025
You could call it the best kept secret for professionals and experts in AI, as you won’t find these books and articles in traditional outlets. Yet, they are read by far more people than documents posted on ArXiv or published in scientific journals, so not really a secret. Actually, one of these books is also published by Elsevier (here), but this is the exception rather than the rule. What makes them different? They are written by an expert with considerable practical experience and successful, large-scale enterprise implementations of the material discussed. While covering state-of-the-art with numerous ground-breaking innovations and enterprise-grade Python code, on occasions covering topics well beyond PhD level, the focus is on explaining efficient technology and better mouse traps, in simple English without jargon. Articles Books The second one is popular among data scientists and AI engineers who want to learn efficient state-of-the-art technology discussed nowhere else, at a fraction of the cost and time commitment required by bootcamps and traditional training. For more books, see here. Other resources See my PowerPoint presentation about xLLM, accessible from here. There is another one about NoGAN — the best tabular data synthesizer with best evaluation metric — posted here. For open-source code, see our GitHub repository, here. We also offer an AI Fellowship with a certification that you can add to your credentials section on your LinkedIn profile, for under $100. Based on the same material. See details here. More articles are posted on the following blogs: For deep technical research papers, see here. To not miss future articles, books and AI resources, sign-up to our free newsletter.
LLMs – Key Concepts Explained in Simple English, with Focus on LLM 2.0
The following glossary features the main concepts attached to LLM 2.0, with examples, rules of thumb, caveats, best practices, contrasted against standard LLMs. For instance, OpenAI has billions of parameters while xLLM, our proprietary LLM 2.0 system has none. This is true if we consider a parameter as a weight connecting neurons in a deep neural network: after all, the xLLM architecture does not rely on neural networks for the most part. Yet, xLLMs has a few dozen intuitive parameters easy to fine-tune, if the word “parameter” is to be interpreted in its native form. The picture below is an extract from the Python code. Agents — Action agents are home-made apps to perform tasks on retrieved data: tabular data synthetization, auto-tagging, auto-indexing, taxonomy creation or augmentation, text clustering for instance for fraud detection, enhanced visualization including data videos, or cloud regression for predictive analytics (whether the data comes from PDFs, Web, databases or data lakes). Tasks not included in this list (code generation, solving math problems) are performed using external agents. Agents — Search agents detect user intent in the prompt (“how to”, “what is”, “show examples” and so on) to retrieve chunks pre-tagged accordingly. Alternatively, they can be made available from the UI, via an agent selection box. Backend — Parameter, tables, and components linked to corpus processing. See also frontend. Card — A clickable summary box in the response featuring one element of the response, corresponding to a chunk and featuring its relevancy score, index, and contextual elements. When you click on it, you get the detailed text, and tables or images if there are any. Chunking — We use hierarchical chunking with two levels. A backend table maps the list of daughter chunks attached to a parent chunk (the key is a parent chunk); xLLM also builds the inverse table where the key is a daughter chunk, and the value is the parent. Contextual elements — Categories, chunk titles, tags, time stamps, document titles in PDF repository, and so on, added to chunks. Their name and content come from the corpus or created post-crawling. Chunks have a variable number of contextual elements. In poorly structured corpuses, chunk titles may be generated based on relative font size or retrieved from a table of content. Since these elements are linked to the entire knowledge graph, xLLM uses an infinite context window. Context window — To compute multi-token correlations or generate c-tokens (multi-tokens with words not glued together but separated by other words) we look at words relatively close to each other within a chunk. This is the finite context window. Its length can be fine-tuned. An efficient algorithm performs this task linearly rather than quadratically depending on the size of the chunk (no inner loops). Crawling — Generic word that means intelligently extracting text and other elements from the corpus to create the xLLM file. If the corpus is a repository of PDF documents, crawling should be interpreted as parsing. If the input data comes from a database, crawling means DB querying, extraction and parsing. Some corpuses have a mix of public and private Web, DB, file repositories (PDFs) and so on. In this case, crawling means intelligently extracting and combining all the information from the various sources, broken down into chunks. See also smart crawling and xLLM file. Distillation — Not to be confused with deep neural network distillation (removing useless weights). Here backend distillation is used to remove duplicate entries in the corpus. Frontend distillation removes duplicates or redundant elements in the response, acting as a response cleaning agent. It can be turned off or customized via frontend parameters. Embeddings — Unlike standard LLMs, embeddings are not needed to build the response. Instead, they are used to suggest multi-tokens related to the prompt, allowing the user to try a different prompt with just one click. We use variable length embeddings and a combination of PMI or E-PMI to measure similarities, rather than cosine similarity. We do not use vector databases or vector search. Instead, we use very fast hash lookups. Fine-tuning — Frontend fine-tuning is offered to power users in real-time with no latency. To the contrary, backend fine-tuning re-creates all the backend tables. Fine-tuning can be global (for all sub-LLMs at once), local (one sub-LLM at a time) or hybrid (with both local and global parameters). Parameters are intuitive: you can predict the impact of increasing or decreasing values or tuning them on or off. The system is based on explainable AI. Fine-tuning allows the response to be customized to the user. See also parameters and self-tuning. Frontend — Parameter, tables, and components linked to prompt processing. Local extracts of the backend tables are used in the frontend to process the prompt efficiently, for instance q_dictionary and q_embeddings. The prefix “q_” indicates that this is a local table containing all that is needed from the backend, to process the prompt (also called “query”). See also backend. Indexing — Mechanism to reference the exact location in the corpus corresponding to a response element or chunk in the xLLM data. We use a multi-index with entries sequentially generated to allow for corpus browsing; the multi-index contains references to the parent chunk and other elements such as font size. Knowledge graph — A set of contextual elements organized as a graph, retrieved from the corpus via smart crawling, such as the taxonomy, breadcrumbs, related items, tags, links, tables, and so on. If the internal knowledge graph is poor, it can be enhanced by adding tags or categories via a labeling algorithm (or clustering), post-crawling. Typically, xLLM does not build a knowledge graph; instead, it uses the one found in the corpus. In some cases, it is augmented or blended with external knowledge graphs. Multimodal — Tables and images are detected at the chunk level and stored separately. They are accessible from cards in the response, along with the other contextual elements (categories, tags, titles). Also, there are special tags pre-assigned to chunks post-crawling, to indicate the presence
What is LLM 2.0?
LLM 2.0 refers to a new generation of large language models that mark a significant departure from the traditional deep neural network (DNN)-based architectures, such as those used in GPT, Llama, Claude, and similar models. The concept is primarily driven by the need for more efficient, accurate, and explainable AI systems, especially for enterprise and professional use cases. The technology was pioneered by Bonding AI under the brand name xLLM. Details are posted here. Key Innovations and Features 1. Architectural Shift 2. Knowledge Graph Integration 3. Enhanced Relevancy and Exhaustivity 4. Specialized Sub-LLMs and Real-Time Customization 5. Deep Retrieval and Multi-Index Chunking 6. Agentic and Multimodal Capabilities Comparison: LLM 2.0 vs. LLM 1.0 Feature LLM 1.0 (Traditional) LLM 2.0 (Next Gen) Core Architecture Deep neural networks, transformers Knowledge graph, contextual retrieval Training Requirements Billions of parameters, GPU-intensive Zero-parameter, no GPU needed Hallucination Risk Present, often requires double-checking Hallucination-free by design Prompt Engineering Often necessary Not required Customization Limited, developer-centric Real-time, user-friendly, bulk options Relevancy/Exhaustivity No user-facing scores, verbose output Normalized relevancy scores, concise Security/Data Leakage Risk of data leakage Highly secure, local processing possible Multimodal/Agentic Limited, mostly text Native multimodal, agentic automation Enterprise and Professional Impact LLM 2.0 is particularly suited for enterprise environments due to: Summary LLM 2.0 represents a paradigm shift in large language model design, focusing on efficiency, explainability, and enterprise-readiness by leveraging knowledge graphs, advanced retrieval, and modular architectures. It aims to overcome the limitations of traditional DNN-based LLMs, offering better ROI, security, and reliability for professional users.
Doing Better with Less: LLM 2.0 for Enterprise
Standard LLMs are trained to predict the next tokens or missing tokens. It requires deep neural networks (DNN) with billions or even trillions of tokens, as highlighted by Jensen Huang, CEO of Nvidia, in his keynote talk at the GTC conference earlier this year. Yet, 10 trillion tokens cover all possible string combinations; the vast majority of them is noise. After all, most people have a vocabulary of about 30k words. But this massive training is necessary to prevent DNNs from getting stuck in sub-optimal configurations due to vanishing gradient and other issues. What if you could do with a million times less? With mere millions of tokens rather than trillions? Afterall, predicting the next token is a task remotely related to what modern LLMs do. Its history is tied to text auto-filling, guessing missing words, autocorrect and so on, developed initially for tools such as BERT. Now, it’s no different than training a plane to efficiently operate on the runway, but not to fly. It also entices LLM vendors to charge clients by token usage, with little regard to ROI. Our approach is radically different. We do not use DNNs nor GPUs. It is as much different from standard AI than it is from classical NLP and machine learning. Its origins are similar to other tools that we built including NoGAN, our alternative to GAN for tabular data synthetization. NoGAN — a fast technology with no DNN — performs a lot faster with much better results, even in real-time. The output quality is assessed using our ground-breaking evaluation metric capturing important defects missed by all other benchmarking tools. In this article, I highlight unique components of xLLM, our new architecture for enterprise. In particular, how it can be faster, not using DNNs or GPUs, yet delivers more accurate results at scale without hallucination while minimizing the need for prompt engineering. No training, smart engine and other components instead The xLLM architecture goes beyond traditional LLMs transformed based models; xLLM is not a pre-trained model. The core components that make it innovative are: 1. Smart Engine The foundation of xLLM is enterprise data (PDFs, web, systems, etc.) The base model does not require training. It retrieves the text “as is” from the corpus along with contextual elements found in the corpus: Tags and categories may be pre-assigned to chunks post-crawling using home-made algorithm if absent in the corpus. The output is structured and displayed as 10 summary boxes called cards and selected out of possibly 50 or more, based on relevancy score. To get full info about a card, the user clicks on it. In the UI, the user can also specify category, sub-LLM, and other contextual elements. As an advanced user, you can leverage the Smart Engine to validate the retrieval process (data, tags, and other contextual elements), fine-tune intuitive parameters based on E-PMI metric (enhanced pointwise mutual information, a flexible alternative to cosine similarity), to adapt relevancy scores, stemming, and so on. Embeddings displayed as a clickable graph allows you to try suggested, related prompts relevant to your query, based on corpus content. 2. Multimodal agents such as synthetic data In Figure 1, the featured agent is for tabular data synthetization. We developed NoGAN synthetic data generation to enrich xLLM. This component depends on the business use case, e.g. a bank wants to enhance the fraud models using xLLM in two stages: Other home-made agents part of xLLM: 3. Response Generator offering user-customized contextual output The user can choose prose for the response (like Perplexity.ai) as opposed to structured output (organized boxes or sections with bullet lists from Smart Engine). In this case, training is needed but not using the whole Internet: typical LLMs have very large tokens lists covering big chunks of the English and other languages, most tokens irrelevant to business context. How is xLLM different from RAG The xLLM architecture is different from RAG. It shows structured output to the user (sections with bullet lists, or cards) that can be turned into continuous text if desired. The chat environment is replaced by selected cards displayed to the user, each with summary information, links and relevancy scores. With one click (the equivalent of a second prompt in standard systems), you get the detailed information attached to a card. Also, alternate clickable prompts are suggested based on corpus content and relevant to your initial queries. Top answers are cached. Of course, the user can still manually enter a new prompt, to obtain deeper results related to his previous prompt (if there are any). Or the user can decide not to link the new prompt to the previous one. A few key points: The user can do exact or broad search, search by recency, enter negative keywords or put higher weight on first multi-token in the prompt; xLLM will look at all combinations of multi-tokens in the prompt (multi-tokens up to 5 words) for 10-word prompts and do so very efficiently with our own technology rather than vector DB. It also looks for synonyms and acronyms to increase exhaustivity. It also has a unique un-stemming algorithm. Real-time fine-tune: LoRA approach LoRA is used to adapt the style of the output but not the knowledge of the LLM. Fine-tune is used both to adapt response and change selection criteria, to optimize output distillation, stemming, E-PMI metric, relevancy scores, various thresholds, speed, and so on. Parameters most frequently favored by users lead to a default parameter set. This is the reinforcement learning part, leading to xLLM self-tuning. Important to mention: Leveraging Explainable AI The overall approach is to be an Open Box (the opposite of a Black Box) able to explain systematically from a prompt perspective everything that happens, as follows. A prompt generates a clickable graph with nodes. The nodes are based on E-PMI threshold and source. By clicking on a node, the user gets domain, sub-domain, tags, chunk or sub-chunk ID, relevancy score, and content: text, images, tables. Key Elements of XAI (explainable AI for xLLM): Dealing with figures,