InTDS ArchivebyDr. Leon EversbergHow to Improve LLM Responses With Better Sampling ParametersA deep dive into stochastic decoding with temperature, top_p, top_k, and min_pSep 2, 20247Sep 2, 20247
InTDS ArchivebyHans Christian EkneExploring the Strategic Capabilities of LLMs in a Risk Game SettingIn a simulated Risk environment, large language models from Anthropic, OpenAI, and Meta showcase distinct strategic behaviors, with Claude…Aug 27, 20248Aug 27, 20248
InTDS ArchivebyKatherine MunroNo Baseline? No Benchmarks? No Biggie! An Experimental Approach to Agile Chatbot DevelopmentLessons learned bringing LLM-based products to productionAug 26, 2024Aug 26, 2024
InTDS ArchivebyLina FaikFrom Text to Networks: The Revolutionary Impact of LLMs on Knowledge GraphsA Step-by-Step Guide to Building and Leveraging Knowledge Graphs with LLMsAug 29, 20242Aug 29, 20242
InTDS ArchivebyOzgur GulerTackle Complex LLM Decision-Making with Language Agent Tree Search (LATS) & GPT-4oEnhancing LLM Decision-Making: Integrating Language Agent Tree Search with GPT-4o for Superior Problem SolvingAug 26, 2024Aug 26, 2024
InTDS ArchivebyAparna DhinakaranNavigating the New Types of LLM Agents and ArchitecturesThe failure of ReAct agents gives way to a new generation of agents — and possibilitiesAug 30, 20249Aug 30, 20249
Vipra SinghLLM Architectures Explained: Word Embeddings (Part 2)Deep Dive into the architecture & building real-world applications leveraging NLP Models starting from RNN to Transformer.Aug 18, 20249Aug 18, 20249
InTDS ArchivebyAlmog BakuThe LLM Triangle Principles to Architect Reliable AI AppsSoftware design principles for thoughtfully designing reliable, high-performing LLM applicationsJul 16, 202414Jul 16, 202414
InTDS ArchivebyDr. Leon EversbergHow to Use Hybrid Search for Better LLM RAG RetrievalBuilding an advanced local LLM RAG pipeline by combining dense embeddings with BM25Aug 11, 20245Aug 11, 20245
Vipra SinghLLM Architectures Explained: NLP Fundamentals (Part 1)Deep Dive into the architecture & building of real-world applications leveraging NLP Models starting from RNN to the Transformers.Aug 15, 202425Aug 15, 202425
InArtificial Intelligence in Plain EnglishbyAndrew BestWhy OpenAI’s “Strawberry” is a GAME CHANGER!This is a big step closer to AGI.Aug 9, 202434Aug 9, 202434
InData Science CollectivebyAhmed BesbesWhat Nobody Tells You About RAGsA deep dive into why RAG doesn’t always work as expected: an overview of the business value, the data, and the technology behind it.Aug 23, 202429Aug 23, 202429
InTowards AIbyMandar Karhade, MD. PhD.Why RAG Applications Fail in ProductionIt worked as a prototype; then all went down!Mar 19, 202430Mar 19, 202430
InTowards AIbyChristopher TaoDo Not Use LLM or Generative AI For These Use CasesChoose correct AI techniques for the right use case familiesAug 10, 202446Aug 10, 202446
InTDS ArchivebyTarik DzekmanWhat Do Large Language Models “Understand”?A deep dive on the meaning of understanding and how it applies to LLMsAug 21, 20248Aug 21, 20248
![Lights emerge out of a laptop with stylised pattern recognition. Text surrounding the laptop: 1. What do Large Language Models “Understand”? 2. Syntax vs Semantics. 3. Just pattern recognition? 4. World Model? -> Take actions in the world. The main text is annotated: [What | Wh-Word] [do | Auxiliary verb] [Large Language Models | Subject] [Understand | Main verb] [? | question mark].](https://miro.medium.com/v2/resize:fill:160:106/1*OruaYLKBOjmpeq5VZH8-Nw.png)
![Lights emerge out of a laptop with stylised pattern recognition. Text surrounding the laptop: 1. What do Large Language Models “Understand”? 2. Syntax vs Semantics. 3. Just pattern recognition? 4. World Model? -> Take actions in the world. The main text is annotated: [What | Wh-Word] [do | Auxiliary verb] [Large Language Models | Subject] [Understand | Main verb] [? | question mark].](https://miro.medium.com/v2/resize:fill:320:214/1*OruaYLKBOjmpeq5VZH8-Nw.png)
