'Nvidia Inside' Can Work in PC Market --- The world's only $5 trillion company can sell the concept that AI computing won't be confined to data centers

 

“The "Intel Inside" marketing campaign made Intel a household name and a ubiquitous personal-computer chip supplier in the 1990s. These days, "Nvidia Inside" has a lot more selling power.

 

That is what Nvidia is betting with its new line of PC chips unveiled on Monday, set to be in Windows-based computers launching this year. With its artificial-intelligence cachet, it is likely that Nvidia will succeed, potentially upending an order in the PC world that has prevailed for pretty much the past five decades.

 

Nvidia's shares jumped more than 6% on Monday, while Windows maker Microsoft rose more than 2%. Shares of PC makers Dell Technologies and HP both surged more than 8%.

 

 Arm Holdings, which licenses the basic blueprints that Nvidia uses in its PC chips, jumped more than 15%.

 

The direct impact of Nvidia's PC play on its finances will likely be limited, given the enormous size of the company's business selling AI chips for data centers. But the move does put Nvidia in a position to supercharge the market for AI-enabled computers and disrupt incumbents in the process.

 

Nvidia has been a big player in PCs for decades through its graphics chips, which produce sharper and smoother images on computer monitors -- a capability videogamers prize. But before detailing its latest chips at a trade show in Taiwan, it hadn't made the central-processing units at the computational hearts of PCs. Intel and Advanced Micro Devices dominate that market.

 

Nvidia's new chips, which combine a CPU with its wildly popular AI-computing hardware, come at a time of weakness for Intel. Intel remains the biggest supplier of CPUs for traditional Windows PCs, with a market share of about 64% in the final quarter of last year, according to Mercury Research. But Intel and other players in the PC market haven't been able to convince huge numbers of consumers or companies to buy new computers because of the AI capabilities of their chips.

 

Around 270 million PCs were sold last year, according to Gartner, up by about 9% from 2024. That isn't a stellar increase amid an AI boom that is supposed to transform how people work and live. The total is below its Covid-era peak of about 340 million in 2021.

 

So far, most sales of AI-enabled PCs to date have been by default. Computer makers are adding neural-processing chips that enable some on-device AI functions to all of their higher-performing product lines.

 

"The buyers choosing AI PCs today aren't necessarily doing so for the AI," said Jitesh Ubrani of market research firm IDC, which tracks PC sales. "They're doing so because, at a certain performance tier, there's no alternative."

 

For Nvidia, PCs are a small part of its business now. But the company boasts a strong appeal in the segment, which bodes well for its latest effort. Nvidia's PC-related revenue jumped 41% in the fiscal year ended January to a little over $16 billion, thanks in part to the introduction of new videogaming chips under the company's popular Blackwell brand. Total PC unit sales grew only 8% during the calendar year, according to IDC data.

 

Whether Nvidia makes further inroads with its PC chips will be a test of the power of its brand and its close association with the AI boom.

 

It will likely be much easier for Nvidia to sell people and companies on new AI-ready computers than it has been for Intel or AMD -- or for that matter, Apple, which also has a popular line of computers that use homegrown chips.

 

Nvidia's rise won't come without challenges -- the largest of which may be the software stickiness that has built up around Intel's and AMD's processors. They use a basic chip architecture called x86. But software that works on x86 processors needs to be adapted to work well on Nvidia's or Apple's Arm-based chips.

 

There is a version of Microsoft's Windows operating system for Arm-based chips, and software engineers have been developing more programs for Arm as the number of Arm-based PCs grows. But Arm as a PC platform remains at a software-development disadvantage vis-a-vis x86, including in areas such as gaming, which remains an important end market for Nvidia.

 

Ultimately though, Nvidia's entrance into CPUs for PCs is likely to further erode the remaining x86 advantage, especially in the world of Windows-based PCs, where it has been difficult for new Arm-based players to gain a foothold.

 

"Intel Inside" worked for many years. But in tech, even memorable marketing slogans have a shelf life.” [A]

 

Arm Holdings licenses the basic blueprints that Nvidia uses. Taiwanese produce the chips. What does Nvidia do? Marketing only?

 

Nvidia operates as a fabless semiconductor company. Far from just doing marketing, they handle the most complex, value-driving phases of hardware and software development: custom architecture design and software ecosystem creation.

Their primary roles include:

1. Architectural Design & Engineering

While Arm provides foundational blueprints (the instruction set architecture), Nvidia engineers take those base concepts and spend years creating their own highly customized circuits, memory pathways, and processing logic (e.g., their GPU architectures and AI accelerators like Blackwell).

2. Software Ecosystems (e.g., CUDA)

Nvidia’s biggest competitive advantage isn't just their chips, but the CUDA Platform—a proprietary software layer that allows developers to actually harness the chips for AI training and general computation. They invest massive amounts into R&D to make sure their hardware works seamlessly with the world's most advanced AI frameworks (like PyTorch [B]).

3. System Integration & Platform Building

Nvidia doesn't just sell individual microchips; they design entire data center motherboards, networking interfaces, and massive computing clusters (like their DGX supercomputers). They figure out how thousands of chips and cooling systems communicate flawlessly at petabyte-per-second speeds.

4. Supply Chain Orchestration

Nvidia manages the incredibly intricate process of feeding their designs to foundries (like TSMC in Taiwan) and advanced packaging specialists, turning microscopic blueprints into billions of physical transistors.

By offloading the incredibly expensive, capital-intensive manufacturing to specialized foundries, Nvidia frees up hundreds of billions in capital to focus entirely on R&D and dominating the software layer.

 

A. 'Nvidia Inside' Can Work in PC Market --- The world's only $5 trillion company can sell the concept that AI computing won't be confined to data centers. Gallagher, Dan; Fitch, Asa.  Wall Street Journal, Eastern edition; New York, N.Y.. 03 June 2026: B11. 

 

B. PyTorch is a highly popular, open-source machine learning framework primarily used for building and training deep neural networks. Created by Meta’s AI Research lab (FAIR) in 2016, it is written in Python and C++, and has become the leading technology for cutting-edge artificial intelligence projects.

Core Pillars of PyTorch

The framework is essentially built around two main foundational features:

•           Tensor Computation: It handles multidimensional arrays (tensors) similar to NumPy, but seamlessly accelerates them using hardware like GPUs and TPUs.

•           Automatic Differentiation (Autograd): It features a built-in engine that tracks mathematical operations on the fly, calculating the precise gradients needed for neural network training automatically.

 

________________________________________

Why PyTorch is Used

PyTorch has become the default choice for major tech organizations (including OpenAI, Tesla, and Microsoft) due to several distinct advantages:

1. Dynamic Computation Graphs

Unlike traditional frameworks that use static graphs (where the model structure must be completely compiled before running), PyTorch uses a Define-by-Run approach. The math graph is built dynamically as the code executes. This allows developers to change neural network behavior at runtime, utilize native Python loops, and scale variable-length data (like text sentences) much more easily.

2. Native Python Integration ("Pythonic")

PyTorch doesn’t feel like a separate language; it integrates cleanly with the Python ecosystem. This makes it highly readable and allows developers to use standard Python packages, object-oriented structuring, and native debugging tools (like pdb or standard IDE breakpoints).

3. Unmatched Research Dominance

Because it favors rapid experimentation, it commands a massive lead in academic research, holding over an 85% research share over competing frameworks like TensorFlow. When new AI models are invented, their source code is almost always published first in PyTorch.

4. Strong Production Capabilities

While highly flexible during research, tools like TorchScript allow developers to serialize PyTorch models into a structured, highly optimized graph mode. This means a model can be designed intuitively in Python, then seamlessly transitioned to a high-speed C++ runtime environment for real-world enterprise deployments.

5. Massive Community & Ecosystem

PyTorch forms the backbone of the generative AI boom. It is the underlying engine for top industry tools, including the [Hugging Face Transformers](https://hugging face.co) library for language processing, TorchVision for image models, and foundational models like OpenAI's ChatGPT and Stable Diffusion.

________________________________________

Common Applications

Developers leverage the official PyTorch Library to power technologies across a variety of fields:

•           Natural Language Processing: Powering Large Language Models (LLMs), machine translation, and text generation.

•           Computer Vision: Driving self-driving car algorithms (like Tesla Autopilot), object detection, and generative art creation.

•           Reinforcement Learning: Crafting complex AI agents for robotics, strategy gaming, and simulation systems.

Dvylika dirbtinio intelekto terminų, kuriuos turėtų žinoti kiekvienas, kad galėtume pigiau naudotis dirbtiniu intelektu

 

„Tikslus derinimas, RAG, vektoriai? Dirbtinis intelektas turi daug techninių terminų, apibūdinančių svarbius pokyčius. Štai apžvalga visiems, norintiems prisijungti prie pokalbio:

 

1. Žetonai

 

Tai yra pagrindinis vienetas, kurį naudoja tokie kalbos modeliai, kaip „ChatGPT“ ar „Google Gemini“. Žodžiai suskirstomi į jų komponentus. Trumpi žodžiai dažnai sudaro vieną žetoną, kaip ir tarpai bei skyrybos ženklai. Ilgi žodžiai suskirstomi į kelis žetonus. Vokiečių kalboje vienas žetonas atitinka maždaug 0,75 žodžio.

 

Kiekvienas kalbos modelis turi ribotą žetonų skaičių, kurį jis gali sekti pokalbio metu. Jei pokalbis tampa per ilgas, dirbtinis intelektas pamiršta pradžią, nes pasiektas maksimalus žetonų skaičius.

 

2. API sąsajos

 

DI modelių API sąsajos yra pagrįstos žetonais. Tai programavimo ryšiai tarp serverių. Jas naudoja daugelis įmonių, norinčių, kad dirbtinis intelektas būtų prieinamas jų darbuotojams, nereikalaujant atskiros paskyros kiekvienam darbuotojui. Mokėti mėnesinį mokestį ilgainiui paprastai yra pigiau. Pavyzdžiui, naudojant GPT-5.2, vienas milijonas žetonų kainuoja 1,75 USD, kaip įvestis, ir 14 USD, kaip išvestis. Taigi, dirbtinio intelekto sistemos atskiria įkeltą informaciją nuo mašininio generavimo duomenų.

 

 

3. Optimizuoti dirbtinio intelekto modeliai

 

 

Jie dažnai turi tokias priesagas, kaip „Instant“, „Flash“ arba „Mini“. Jiems reikia mažiau skaičiavimo galios ir jie kainuoja mažiau.

 

Pavyzdžiui, naudodami „Gemini 2.5 Flash“, galite nusiųsti dirbtiniam intelektui dokumentą su 750 000 žodžių, o tai atitinka maždaug vieną milijoną įvesties žetonų ir kainuoja maždaug 0,30 USD. Jei užklausiate ilgos, maždaug 7500 žodžių santraukos per raginimą (tai atitinka maždaug 10 000 išvesties žetonų), tai kainuoja 0,025 USD, taigi užduotis iš viso kainuoja mažiau, nei 33 centus.

 

 

4. Vektoriai

 

 

Vektoriai, taip sakant, yra pagrindinis dirbtinio intelekto skaičiavimo vienetas. Jie verčia žodžius koordinatėmis. Paprastoje dvimatėje vektorinėje diagramoje DI gali rūšiuoti objektus pagal savybes „dydis“ ir „pūkuotumas“, pavyzdžiui, šunį įterpti į koordinates 0,8 ir 0,7, katę – į koordinates 0,3 ir 0,9, o skrudintuvą – į koordinates 0,4 ir 0,0. DI iš karto mato: šuo ir katė yra arti vienas kito, skrudintuvas – toli. Realiuose DI modeliuose šie vektoriai dažnai turi ne du, o šimtus ar tūkstančius matmenų. Tai leidžia DI rasti šunį, kai jo paklausiama apie „lojantį augintinį“, nes terminas „šuo“ yra saugomas daugiamatėje vektorinėje duomenų bazėje [1], o jo koordinatės yra labai artimos terminams „augintinis“ ir „lojimas“.

 

 

5. Tikimybės

 

 

Jos sudaro dirbtinio intelekto pagrindą. Kokia tikimybė, kad šalia žodžio „mėnulis“ atsiras vardas Neilas Armstrongas? Tai tikrai labiau tikėtina, nei dainininko Louiso Armstrongo vardas. Vektorius paverčia žmonių kalbos neapibrėžtumą griežta matematika ir suteikia kiekvienai sąvokai tikslų adresą didžiulėje prasmių visatoje.

 

6. Haliucinacijos

 

Jos kyla, kai mašina negali aiškiai suprasti reikšmės. Dirbtinis intelektas nežino, kas yra tiesa. Jis žino tik tai, kas tikėtina. Neteisingi, sugalvoti, atsakymai dažnai atsiranda dėl žinių spragų, kurias dirbtinis intelektas užpildo panašių temų modeliais. Be to, dirbtinis intelektas dažnai stengiasi įtikti vartotojui, o ne jį taisyti. Pagal numatytuosius nustatymus dirbtinio intelekto sistemos yra apmokytos būti naudingos. Atsakymas, pvz., „Nežinau“, atsakymo algoritmui dažnai atrodo mažiau naudingas, nei sugalvotas atsakymas. Tai galima ištaisyti: „Atsakykite į klausimą tik remdamiesi patikrintais faktais. Jei nesate tikri dėl atsakymo arba jūsų mokymo duomenyse trūksta informacijos, atsakykite: „Neturiu jokių patikrintų duomenų apie tai.“ Nespėliokite.”

 

7. Temperatūra

 

DI temperatūra yra naudingas nustatymas profesionalioje aplinkoje, norint perjungti tikslius atsakymus į vaizduotės išklotines. Temperatūra svyruoja nuo nulio iki dviejų. Pavyzdžiui, faktinių tekstų (arti faktų) analizei naudotumėte 0 arba 0,1 temperatūrą, o rašydami romaną, norėtumėte lavinti vaizduotę, todėl nustatytumėte, tarkime, 1,5 temperatūrą. Profesionalioje aplinkoje šiuos parametrus galima reguliuoti, naudojant slankiklius; paprastuose pokalbiuose tai veikia ir teksto lygmeniu: „Imituokite 0,0 temperatūrą. Būkite itin tikslūs, glausti ir venkite bet kokio kūrybinio pagražinimo.“

 

8. RAG

 

RAG sistema yra dar vienas būdas užkirsti kelią dirbtiniam intelektui (DI) pasinerti į haliucinacijas. RAG yra „Išgavimo papildytas generavimas“ (angl.), jis reiškia generavimą, pagerintą per paiešką. DI pateikiamas užrakintas žinių rinkinys, iš kurio jis gali išimtinai semtis žinių. Visi pateikti duomenys dokumentai ir jų turinys konvertuojami tada į vektorius. Kai DI užduodamas klausimas, jis naudoja vektorius, kad surastų matematiškai panašiausią turinį.

 

Ypač patogi RAG naudojimui yra „Google“ užrašų knygelė LM: į vieną užrašų knygelę galite įkelti iki 300 dokumentų ir tada pateikti DI užklausą dėl išsamesnės informacijos.

 

Kita vertus, įmonės dažnai naudoja vektorinių duomenų bazių teikėjus, kurie gali talpinti dešimtis tūkstančių ar daugiau dokumentų, juos suskaidyti į atskiras dalis ir vektorizuoti.

 

 

9. Tikslinimas

 

 

Tikslinimas apima daugiau, nei RAG sistemą. Tai apima esamo modelio, pvz., GPT-4o, perkvalifikavimą su konkrečiu, nedideliu duomenų rinkiniu. Tai leidžia DI išmokti, pavyzdžiui, kopijuoti asmens rašymo stilių ir rašyti medicinines ataskaitas pagal konkrečias gaires.

 

Mašina gauna daugybę pavyzdžių klausimų ir atsakymų formatu, naudojant, pavyzdžiui, „OpenAI“ prietaisų skydelį arba „Google Cloud Vertex“ DI.

 

Po mokymo gaunate „pasirinktinį“ DI modelį. Vienkartinis tikslus derinimas reikalauja papildomų žetonų, o užklausos jūsų „pasirinktiniam“ dirbtiniam intelektui (DI) – dar daugiau.

 

Įprastas daugelio įmonių naudojamas triukas vadinamas distiliavimu: aukštos kokybės, brangus modelis, pvz., „GPT-5.2 Pro“, iš pradžių sugeneruoja apie 500 tobulų klausimų ir atsakymų porų. Šie 500 atsakymų vėliau naudojami mažam, nebrangiam modeliui, pvz., „Gemini Flash-Lite“, tiksliai suderinti. Nuo tada aukštos kokybės atsakymai saugomi nebrangiame modelyje.

 

 

10. Agentai

 

 

Vadinamieji DI agentai yra konkrečių užduočių specialistai. Jų elgesį apibrėžia vaidmuo („Jūs esate kelionių ekspertas“), jų žinios plečiamos dešimčia ar dvidešimties dokumentų arba prisijungiant prie RAG sistemos. Agentas gali suskaidyti užduotis į dalines užduotis. Ir pagal iš anksto nustatytus parametrus jis gali modifikuoti duomenis, pavyzdžiui, kurdamas kalendoriaus įrašus arba rengdamas ir išsaugodamas el. laiškus.

 

 

11. Sistemos instrukcija

 

 

Tai apibrėžia, ką DI agentas turėtų mokėti daryti. „ChatGPT“ galite nustatyti savo agentus skiltyje „Naršyti GPT“. Ten jūs jam suteikiate vardą (pvz., „Rašybos patarėjas Frobenas“) ir trumpą aprašymą: „Tikrina tekstų rašybą vokiečių kalba ir atlieka pataisymus“. Po to pateikiamos išsamios instrukcijos agentui, pavyzdžiui: „Rašybos patarėjas Frobenas tikrina tekstų rašybą vokiečių kalba pagal Vokiečių kalbos rašybos tarybos taisykles. Jis gauna tekstus, patikrina juos pagal oficialias taisykles ir tada sukuria lentelę. Lentelėje yra originalus sakinys, pataisytas sakinys ir atliktų pakeitimų paaiškinimas. Jis paaiškina, kodėl atliko pakeitimus, ir pateikia aiškius bei tikslius pagrindimus. Jis atkreipia dėmesį į taisyklingą rašybą, gramatiką ir skyrybą bei užtikrina, kad tekstai atitiktų dabartinius standartus. Klaidos neleidžiamos. Rašybos patarėjas Frobenas kalba normaliai ir mandagiai. Rašybos tarybos taisyklės pateiktos įkeltame dokumente „RfdR_Amtliches-Regelwerk_2024-komprimiert.pdf'“. Remkitės tik šiuo dokumentu, jokiais kitais šaltiniais.“ Šis mūsų rašybos agentas yra viešai prieinamas „ChatGPT“ platformoje ir nuo pat pradžių ištaisė kelis tūkstančius „ChatGPT“ vartotojų dokumentų. Kaip agentų kūrėjai, negalime peržiūrėti įkeltų dokumentų ir pokalbių istorijos.

 

12. Daugiagentės sistemos

 

Šios sistemos leidžia bendradarbiauti keliems agentams. Agentas R yra atsakingas už tyrimus, agentas K veikia, kaip kritikas, o agentas M yra vadovas, kuris viską apibendrina ir pateikia. Šie agentai bendrauja tarpusavyje ir keičiasi tarpiniais rezultatais. Tokių agentų organizavimui reikalinga moderni agentų platforma, pvz., „Langdock“ arba „Nuwacom“. Šios platformos naudoja įprastas dirbtinio intelekto sistemas per API sąsajas ir, be dedikuotų agentų, leidžia greitai kurti duomenų bazes, kuriomis galima dalytis tarp kolegų, ir kurti žinių bazes, prieinamas darbuotojams ir dirbtinio intelekto agentams.” [2]

 

1. Vektorinėje duomenų bazėje saugomi galutiniai arba tarpiniai modelio rezultatai, konkrečiai – vektoriniai įterpimai, vaizduojantys jūsų dokumentus, vaizdus ar mediją. Atgalinį platinimą galite laikyti „gamybos procesu“, kurio metu sukuriamas vektorinis įterpimas, o vektorinę duomenų bazę – „sandėliu“, kuriame tie baigti įterpimai tvarkomi, kad būtų galima greitai juos rasti. 

 

2. Zwölf KI-Begriffe, die jeder kennen sollte. Frankfurter Allgemeine Zeitung; Frankfurt. 02 Mar 2026: 18.   Von Marcus Schwarze

Twelve AI Terms Everyone Should Know in Order to Save Money Using AI

“Fine-tuning, RAG, vectors? The field of artificial intelligence is full of technical terms describing key developments. Here is an overview for anyone who wants to be able to join the conversation:

 

1. Tokens

 

These are the fundamental units utilized by a language model such as ChatGPT or Google Gemini. In this process, words are broken down into their constituent parts. Short words often correspond to a single token, as do spaces and punctuation marks. Longer words are split into multiple tokens. In the German language, one token corresponds to approximately 0.75 words.

 

Every language model has a token limit—the maximum amount of information it can "keep in mind" during a chat session. If a conversation becomes too lengthy, the AI ​​ will begin to "forget" the beginning of the dialogue once this maximum token count has been reached.

 

2. API Interfaces

 

The API interfaces for AI models are billed based on token usage.

 

These interfaces serve as programming connections between servers. They are widely adopted by companies wishing to provide their employees with access to artificial intelligence without having to pay a fixed monthly subscription fee for every single employee. Ultimately, this approach usually proves to be more cost-effective.

 

For instance, with GPT-5.2, one million tokens cost $1.75 for input and $14.00 for output.

 

 Thus, AI systems distinguish between information uploaded by the user and information generated by the machine itself.

 

3. Optimized AI Models

 

These models often feature suffixes such as "Instant," "Flash," or "Mini." They require less computing power and come at a lower cost.

 

For example, with Gemini 2.5 Flash, you can submit a document containing 750,000 words to the AI—which corresponds to roughly one million input tokens—at a cost of approximately $0.30. If you then use a prompt to request a lengthy summary of around 7,500 words (equivalent to approximately 10,000 output tokens), the cost is $0.025; in total, therefore, the entire task comes to less than 33 cents.

 

4. Vectors

 

Vectors are, so to speak, the fundamental computational units of artificial intelligence systems. They translate words into coordinates. On a simple two-dimensional vector map, for instance, an AI might—sorting by the properties "size" and "fluffiness"—place a dog at coordinates (0.8, 0.7), a cat at (0.3, 0.9), and a toaster at (0.4, 0.0). The AI ​​immediately sees: the dog and the cat are located close to one another, while the toaster is far away. In real-world AI models, these vectors do not have just two dimensions, but often hundreds or thousands. This makes it possible for an AI—when asked for a "barking pet"—to identify a dog, because, as a concept within the multidimensional vector database [1], its coordinates are stored in very close proximity to the concepts of "pet" and "barking."

 

5. Probabilities

 

These form the underlying framework of artificial intelligence systems. How probable is it that the name Neil Armstrong will appear in the vicinity of the word "Moon"? It is, in any case, more probable than the name of the singer Louis Armstrong. A vector transforms the imprecise nature of human language into rigorous mathematics. It assigns every concept a precise address within a vast universe of meaning.

 

6. Hallucinations

 

These occur when the machine is unable to extract meaning with sufficient clarity.

 

An AI does not know what is true; it knows only what is probable.

 

The root cause of incorrect or fabricated answers is often a gap in knowledge, which the AI ​​attempts to fill using patterns it has learned from similar topics. Furthermore, an AI often attempts to please the user rather than correct them. By default, AI systems are trained to be helpful; consequently, the response "I don't know" often appears less helpful to the response-generation algorithm than a fabricated answer. This behavior can be trained out of the machine—for instance, by instructing it: "Answer the question solely on the basis of verified facts." "If you do not know the answer with certainty, or if the information is missing from your training data, respond with: 'I do not have any verified data available regarding this.' Do not guess."

 

7. Temperature

 

The AI's "temperature" is a useful dial in professional environments for shifting the balance between precise answers and imaginative elaborations. Temperature values ​​typically range between zero and two. For instance, one would likely use a temperature of 0 or 0.1 for an analytical text analysis (keeping strictly to the facts), whereas when writing a novel—where the goal is to foster creativity—one might set the temperature to, say, 1.5. In professional environments, these parameters can be adjusted via sliders; in simpler chat interfaces, this can also be achieved through text-based instructions: "Simulate a temperature of 0.0. Be extremely precise and concise, and avoid any form of creative embellishment."

 

8. RAG

 

A RAG system offers another method for curbing AI hallucinations. "Retrieval-Augmented Generation" refers to a generation process enhanced by information retrieval. The AI ​​is provided with a confined knowledge base from which it is instructed to draw exclusively. In this process, all provided documents and their contents are converted into vectors. If a query is then posed to the AI, it uses these vectors to search for the mathematically most similar content.

 

 A particularly user-friendly RAG tool is Google’s NotebookLM: users can upload up to 300 documents into a single notebook and subsequently query the AI ​​for specific details.

 

Companies, conversely, frequently utilize vector database providers capable of ingesting tens of thousands of documents—or more—breaking them down into constituent parts, and vectorizing them.

 

9. Fine-tuning

 

Fine-tuning goes a step beyond a standard RAG system. It involves retraining an already fully developed model—such as GPT-4o—using a small, specialized dataset.

 

Through this process, the AI ​​learns, for instance, to mimic a specific human writing style or to generate medical reports according to precise specifications.

 

To achieve this, the machine is fed numerous examples in a question-and-answer format—a process that can be carried out via the OpenAI Dashboard or Google Cloud Vertex AI.

 

Upon completion of this training, the result is a "custom" AI model. The one-time fine-tuning process incurs additional token costs, and querying this "custom" AI subsequently requires a higher token expenditure.

 

A common strategy employed by many companies is known as "distillation": a high-quality, high-cost model—such as GPT-5.2 Pro—is first used to generate approximately 500 perfect question-and-answer pairs. These 500 responses are then utilized to fine-tune a tiny, low-cost model—such as Gemini Flash-Lite. From that point forward, the high-quality responses are embedded within the low-cost model.

 

10. Agents

 

So-called AI agents are specialists designed for specific tasks. Their behavior is defined by an assigned role (e.g., "You are a travel expert"), while their knowledge base is expanded through the addition of 10 or 20 documents, or by integrating them with a RAG system. The agent possesses the capability to break down complex tasks into smaller sub-tasks. Furthermore, operating within a set of predefined guardrails, it can modify data—for instance, by creating calendar entries or by drafting and saving email messages.

 

11. System Instruction

 

This defines the capabilities expected of the AI ​​agent. In ChatGPT, custom agents can be set up within the "Explore GPTs" section.

 

There, you assign the agent a name (for instance, "Spelling Advisor Froben") and provide a brief description: "Checks texts for German spelling and provides corrections." This is followed by a detailed set of instructions for the agent—for example: "Spelling Advisor Froben checks texts for German spelling in accordance with the rules established by the Council for German Orthography. It accepts texts, verifies them against the official rulebooks, and subsequently generates a table. This table contains the original sentence, the corrected sentence, and an explanation of the changes made. It explains *why* specific changes were implemented, offering clear and precise justifications. It ensures correct spelling, grammar, and punctuation, thereby guaranteeing that the texts adhere to current standards. No errors are permitted. Spelling Advisor Froben communicates in a normal, friendly tone. The rules governing the Spelling Advisor are contained within the uploaded document 'RfdR_Amtliches-Regelwerk_2024-komprimiert.pdf'. Refer exclusively to this document; do not consult any other sources." Our Spelling Advisor is publicly accessible within ChatGPT and, since its initial release, has corrected thousands of documents submitted by ChatGPT users. As the agent's creators, we are unable to view the uploaded documents or the conversation logs.

 

12. Multi-Agent Systems

 

These systems enable multiple agents to collaborate. Agent R is responsible for research, Agent K acts as a critic, and Agent M serves as the manager, summarizing and presenting the final results. These agents communicate with one another, passing intermediate results back and forth.

 

Orchestrating such agents requires a modern agent platform, such as Langdock or Nuwacom.

 

They utilize standard AI systems via API interfaces, and—in addition to proprietary agents—they allow for prompt databases that can be shared among colleagues, as well as the creation of knowledge bases intended to be accessible to both employees and the AI ​​agents.” [2]

 

1.  What gets stored in the vector database are the final or intermediate outputs of the model—specifically, the vector embeddings representing your documents, images, or media. You can think of backpropagation as the "manufacturing process" that builds the vector embedding, and the vector database as the "warehouse" where those finished embeddings are organized for fast retrieval.

 

2. Zwölf KI-Begriffe, die jeder kennen sollte. Frankfurter Allgemeine Zeitung; Frankfurt. 02 Mar 2026: 18.   Von Marcus Schwarze

Dental care in EU

 

Denmark's new centrist coalition government has pledged to introduce free dental care for all adults within the next 10 years. The ambitious timeline was negotiated as part of the ruling coalition's welfare and cost-of-living policy platform.

Before this system-wide rollout is completed, public dental care in Denmark covers the following groups:

•          Under 22: Dental care is completely free for children and young adults until their 22nd birthday.

•          Adults: Certain preventive treatments and fillings are partially subsidized by public health insurance, but major procedures like crowns and dentures remain fully out-of-pocket.

•          Financially vulnerable: Residents receiving certain social benefits or pensions may be eligible for municipal grants covering a large portion of their expenses.

 

For exact details on what services are currently covered and eligibility criteria, you can check the Lifeindenmark Financial Support Guide or the official EU European Health Insurance Card Denmark Guidelines.