📌 How to Turn Your LLM Prototype into a Production-Ready System
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-03 | ⏱️ Read time: 15 min read
Transforming a promising LLM prototype into a production-ready system involves significant engineering challenges. This guide outlines the essential steps and best practices for moving beyond the experimental phase, focusing on building scalable, reliable, and efficient LLM applications for real-world deployment. Learn how to successfully operationalize your language model from concept to production.
#LLM #MLOps #ProductionAI #LLMOps
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-03 | ⏱️ Read time: 15 min read
Transforming a promising LLM prototype into a production-ready system involves significant engineering challenges. This guide outlines the essential steps and best practices for moving beyond the experimental phase, focusing on building scalable, reliable, and efficient LLM applications for real-world deployment. Learn how to successfully operationalize your language model from concept to production.
#LLM #MLOps #ProductionAI #LLMOps
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100+ LLM Interview Questions and Answers (GitHub Repo)
Anyone preparing for #AI/#ML Interviews, it is mandatory to have good knowledge related to #LLM topics.
This# repo includes 100+ LLM interview questions (with answers) spanning over LLM topics like
LLM Inference
LLM Fine-Tuning
LLM Architectures
LLM Pretraining
Prompt Engineering
etc.
🖕 Github Repo - https://github.com/KalyanKS-NLP/LLM-Interview-Questions-and-Answers-Hub
https://xn--r1a.website/DataScienceM✅
Anyone preparing for #AI/#ML Interviews, it is mandatory to have good knowledge related to #LLM topics.
This# repo includes 100+ LLM interview questions (with answers) spanning over LLM topics like
LLM Inference
LLM Fine-Tuning
LLM Architectures
LLM Pretraining
Prompt Engineering
etc.
https://xn--r1a.website/DataScienceM
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Forwarded from Machine Learning with Python
DS Interview.pdf
1.6 MB
Data Science Interview questions
#DeepLearning #AI #MachineLearning #NeuralNetworks #DataScience #DataAnalysis #LLM #InterviewQuestions
https://xn--r1a.website/CodeProgrammer
#DeepLearning #AI #MachineLearning #NeuralNetworks #DataScience #DataAnalysis #LLM #InterviewQuestions
https://xn--r1a.website/CodeProgrammer
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🗂 Building our own mini-Skynet — a collection of 10 powerful AI repositories from big tech companies
1. Generative AI for Beginners and AI Agents for Beginners
Microsoft provides a detailed explanation of generative AI and agent architecture: from theory to practice.
2. LLMs from Scratch
Step-by-step assembly of your own GPT to understand how LLMs are structured "under the hood".
3. OpenAI Cookbook
An official set of examples for working with APIs, RAG systems, and integrating AI into production from OpenAI.
4. Segment Anything and Stable Diffusion
Classic tools for computer vision and image generation from Meta and the CompVis research team.
5. Python 100 Days and Python Data Science Handbook
A powerful resource for Python and data analysis.
6. LLM App Templates and ML for Beginners
Ready-made app templates with LLMs and a structured course on classic machine learning.
If you want to delve deeply into AI or start building your own projects — this is an excellent starting kit.
tags: #github #LLM #AI #ML
➡️ https://xn--r1a.website/CodeProgrammer
1. Generative AI for Beginners and AI Agents for Beginners
Microsoft provides a detailed explanation of generative AI and agent architecture: from theory to practice.
2. LLMs from Scratch
Step-by-step assembly of your own GPT to understand how LLMs are structured "under the hood".
3. OpenAI Cookbook
An official set of examples for working with APIs, RAG systems, and integrating AI into production from OpenAI.
4. Segment Anything and Stable Diffusion
Classic tools for computer vision and image generation from Meta and the CompVis research team.
5. Python 100 Days and Python Data Science Handbook
A powerful resource for Python and data analysis.
6. LLM App Templates and ML for Beginners
Ready-made app templates with LLMs and a structured course on classic machine learning.
If you want to delve deeply into AI or start building your own projects — this is an excellent starting kit.
tags: #github #LLM #AI #ML
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🚀 Why Modern AI Runs on GPUs and TPUs Instead of CPUs 🤖
AI models are essentially large matrix multiplication engines 🧮.
Training and inference involve billions or even trillions of tensor operations like:
👉 [Input Tensor] × [Weight Matrix] = Output ⚡️
The speed of these computations depends heavily on the hardware architecture 🏗.
Traditional CPUs execute operations sequentially ⏳. A few powerful cores handle tasks one after another. This design is excellent for general purpose computing but inefficient for massive tensor workloads 🐢.
Example:
A transformer model performing attention calculations may require billions of multiplications. A CPU processes them sequentially which increases latency 🐌.
👉 GPUs solve this with parallelism 🚀
GPUs contain thousands of smaller cores designed to execute many matrix operations simultaneously. Instead of one operation at a time, thousands run in parallel 🔄.
Example:
Training a CNN for image classification:
- CPU training time → several hours ⏰
- GPU training time → minutes ⚡️
Frameworks like PyTorch and TensorFlow leverage CUDA cores to parallelize tensor computations across thousands of threads 🔧.
👉 TPUs go even further 🛸
TPUs are purpose built accelerators for deep learning workloads. They use systolic array architecture optimized for dense matrix multiplication 📐.
Instead of sending data back and forth between memory and compute units, data flows directly through a grid of processing elements 🌊.
Example:
Large language models like BERT or PaLM run inference much faster on TPUs due to optimized tensor pipelines 🚄.
Typical latency differences ⏱️
CPU → Seconds
GPU → Milliseconds
TPU → Microseconds
As models scale to billions of parameters, hardware architecture becomes the real bottleneck 🚧.
That is why modern AI infrastructure relies on GPU clusters and TPU pods to train and serve large models efficiently 🏢.
💡Key takeaway
AI progress is not only about better algorithms 🧠. It is also about better compute architecture 🔌.
#AI #MachineLearning #DeepLearning #GPUs #TPUs #LLM #DataScience
#ArtificialIntelligence
AI models are essentially large matrix multiplication engines 🧮.
Training and inference involve billions or even trillions of tensor operations like:
👉 [Input Tensor] × [Weight Matrix] = Output ⚡️
The speed of these computations depends heavily on the hardware architecture 🏗.
Traditional CPUs execute operations sequentially ⏳. A few powerful cores handle tasks one after another. This design is excellent for general purpose computing but inefficient for massive tensor workloads 🐢.
Example:
A transformer model performing attention calculations may require billions of multiplications. A CPU processes them sequentially which increases latency 🐌.
👉 GPUs solve this with parallelism 🚀
GPUs contain thousands of smaller cores designed to execute many matrix operations simultaneously. Instead of one operation at a time, thousands run in parallel 🔄.
Example:
Training a CNN for image classification:
- CPU training time → several hours ⏰
- GPU training time → minutes ⚡️
Frameworks like PyTorch and TensorFlow leverage CUDA cores to parallelize tensor computations across thousands of threads 🔧.
👉 TPUs go even further 🛸
TPUs are purpose built accelerators for deep learning workloads. They use systolic array architecture optimized for dense matrix multiplication 📐.
Instead of sending data back and forth between memory and compute units, data flows directly through a grid of processing elements 🌊.
Example:
Large language models like BERT or PaLM run inference much faster on TPUs due to optimized tensor pipelines 🚄.
Typical latency differences ⏱️
CPU → Seconds
GPU → Milliseconds
TPU → Microseconds
As models scale to billions of parameters, hardware architecture becomes the real bottleneck 🚧.
That is why modern AI infrastructure relies on GPU clusters and TPU pods to train and serve large models efficiently 🏢.
💡Key takeaway
AI progress is not only about better algorithms 🧠. It is also about better compute architecture 🔌.
#AI #MachineLearning #DeepLearning #GPUs #TPUs #LLM #DataScience
#ArtificialIntelligence
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They cover the entire spectrum: classic ML, LLM, and generative models — with theory and practice.
tags: #python #ML #LLM #AI
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🤖 Designing an RAG with search for 10 million documents while minimizing hallucinations 📚
1️⃣ Document ingestion and normalization 📄
Removing duplicates, converting to a single format, extracting metadata, and maintaining versioning. 🔄
2️⃣ Hybrid search (BM25 + vector representations) 🔍
BM25 handles exact keyword matches, while vector search handles semantic relevance. One approach without the other typically suffers from low accuracy at this scale. 📉
3️⃣ Approximate nearest neighbor search + re-ranking ⚖️
Approximate nearest neighbor search quickly retrieves candidates from millions of fragments. Next, a ranking model recalculates relevance through a more rigorous comparison of the query and fragments. 🧠
4️⃣ Trust scoring for sources 🛡️
Each fragment receives an evaluation based on freshness, source reliability, overlap, and consistency with other found results. Data with low trust should not significantly influence the final response. 🚫
5️⃣ Generation with strict context constraints 🚧
The model only operates within the extracted context. Adding knowledge outside the context is prohibited by the pipeline logic. 🚫
6️⃣ Answers with source attribution 📝
Every significant statement must refer to a specific fragment, document, or timestamp. ⏰
7️⃣ Fallback for low search confidence 📉
If the total context confidence falls below a threshold, a response like "not enough data" is returned. 🛑
8️⃣ Continuous quality checks 🧪
Running attack queries, measuring search completeness, testing for hallucinations, and monitoring ranking degradation. 📊
9️⃣ Caching and memory layer 💾
Frequent queries and search chains are cached to reduce latency and computational cost. ⚡
🔟 Observability at all stages 👁️
Tracing the query path, fragment ranking, and the impact of tokens and failure points. 🛠️
🚀 At the scale of 10 million documents, search quality becomes a more critical factor than the choice of generative model.
#RAG #AI #Search #LLM #DataEngineering #Tech
1️⃣ Document ingestion and normalization 📄
Removing duplicates, converting to a single format, extracting metadata, and maintaining versioning. 🔄
2️⃣ Hybrid search (BM25 + vector representations) 🔍
BM25 handles exact keyword matches, while vector search handles semantic relevance. One approach without the other typically suffers from low accuracy at this scale. 📉
3️⃣ Approximate nearest neighbor search + re-ranking ⚖️
Approximate nearest neighbor search quickly retrieves candidates from millions of fragments. Next, a ranking model recalculates relevance through a more rigorous comparison of the query and fragments. 🧠
4️⃣ Trust scoring for sources 🛡️
Each fragment receives an evaluation based on freshness, source reliability, overlap, and consistency with other found results. Data with low trust should not significantly influence the final response. 🚫
5️⃣ Generation with strict context constraints 🚧
The model only operates within the extracted context. Adding knowledge outside the context is prohibited by the pipeline logic. 🚫
6️⃣ Answers with source attribution 📝
Every significant statement must refer to a specific fragment, document, or timestamp. ⏰
7️⃣ Fallback for low search confidence 📉
If the total context confidence falls below a threshold, a response like "not enough data" is returned. 🛑
8️⃣ Continuous quality checks 🧪
Running attack queries, measuring search completeness, testing for hallucinations, and monitoring ranking degradation. 📊
9️⃣ Caching and memory layer 💾
Frequent queries and search chains are cached to reduce latency and computational cost. ⚡
🔟 Observability at all stages 👁️
Tracing the query path, fragment ranking, and the impact of tokens and failure points. 🛠️
🚀 At the scale of 10 million documents, search quality becomes a more critical factor than the choice of generative model.
#RAG #AI #Search #LLM #DataEngineering #Tech
❤6
Forwarded from Machine Learning with Python
Data Science Interview Questions.pdf
1.4 MB
Data Science Interview Questions
💡 Here is your curated list for Data Science interviews!
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
#DataScience #AI #MachineLearning #LLM #TechJobs #InterviewPrep
💡 Here is your curated list for Data Science interviews!
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
#DataScience #AI #MachineLearning #LLM #TechJobs #InterviewPrep
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Parallax: A Parameterized Local Linear Attention That Keeps Softmax and Adds a Learned Covariance Correction Branch 🧠✨
The Transformer’s attention mechanism has barely changed since 2017. Most efficiency work has tried to replace softmax attention outright. A new paper takes a different route. It keeps softmax attention and bolts on a correction branch. 🔄
A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention called ‘Parallax’ that scales to LLM pretraining and codesigns with Muon. 🎓
Parallax does not chase efficiency by cutting compute. It adds compute deliberately, then makes that compute cheaper to run on modern GPUs. 💻⚡
More: https://www.marktechpost.com/2026/05/31/parallax-a-parameterized-local-linear-attention-that-keeps-softmax-and-adds-a-learned-covariance-correction-branch/
#Parallax #LLM #AI #DeepLearning #Transformer #TechNews
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
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The Transformer’s attention mechanism has barely changed since 2017. Most efficiency work has tried to replace softmax attention outright. A new paper takes a different route. It keeps softmax attention and bolts on a correction branch. 🔄
A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention called ‘Parallax’ that scales to LLM pretraining and codesigns with Muon. 🎓
Parallax does not chase efficiency by cutting compute. It adds compute deliberately, then makes that compute cheaper to run on modern GPUs. 💻⚡
More: https://www.marktechpost.com/2026/05/31/parallax-a-parameterized-local-linear-attention-that-keeps-softmax-and-adds-a-learned-covariance-correction-branch/
#Parallax #LLM #AI #DeepLearning #Transformer #TechNews
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
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Multi-Label Text Classification with Scikit-LLM 📝
In this article, you will learn how to perform multi-label text classification using large language models and the scikit-LLM library, without the need for labeled training data or complex model training. 🚀
Topics we will cover include:
What multi-label classification is and why it matters for nuanced text analysis. 📊
How to set up and configure scikit-LLM with a free, open-source LLM from Groq for zero-shot inference. ⚙️
How to load a real-world dataset and run multi-label sentiment predictions using a familiar scikit-learn-style workflow. 📈
Read: https://machinelearningmastery.com/multi-label-text-classification-with-scikit-llm/ 🔗
#ScikitLLM #TextClassification #LLM #MachineLearning #ZeroShot #DataScience
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In this article, you will learn how to perform multi-label text classification using large language models and the scikit-LLM library, without the need for labeled training data or complex model training. 🚀
Topics we will cover include:
What multi-label classification is and why it matters for nuanced text analysis. 📊
How to set up and configure scikit-LLM with a free, open-source LLM from Groq for zero-shot inference. ⚙️
How to load a real-world dataset and run multi-label sentiment predictions using a familiar scikit-learn-style workflow. 📈
Read: https://machinelearningmastery.com/multi-label-text-classification-with-scikit-llm/ 🔗
#ScikitLLM #TextClassification #LLM #MachineLearning #ZeroShot #DataScience
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
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Forwarded from Machine Learning with Python
10 GitHub repositories that are worth checking out for an AI engineer 🤖
1. Hands-On AI Engineering 🛠️
A collection of AI applications and agent systems with practical use cases of LLM.
👉 https://github.com/Sumanth077/Hands-On-AI-Engineering
2. Hands-On Large Language Models 📘
Full code from the book Hands-On Large Language Models: from basics to fine-tuning.
👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
3. AI Agents for Beginners 🎓
A free course from Microsoft with 11 lessons on creating AI agents.
👉 https://github.com/microsoft/ai-agents-for-beginners
4. GenAI Agents 🤖
A large collection of tutorials and implementations of agent systems.
👉 https://github.com/NirDiamant/GenAI_Agents
5. Made With ML 🚀
About the development, deployment, and support of production-ready ML systems.
👉 https://github.com/GokuMohandas/Made-With-ML
6. Learn Harness Engineering ⚙️
A practical course on Harness Engineering for AI agents.
👉 https://github.com/walkinglabs/learn-harness-engineering
7. AutoResearch 🔬
Autonomous cycles of ML experiments from Andrej Karpathy.
👉 https://github.com/karpathy/autoresearch
8. Designing Machine Learning Systems 📚
Notes and materials from Chip Huyen's book.
👉 https://github.com/chiphuyen/dmls-book
9. Awesome LLM Inference ⚡
A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.
👉 https://github.com/xlite-dev/Awesome-LLM-Inference
10. LLM Course 🗺️
A practical course on LLM with a roadmap and Colab notebooks.
👉 https://github.com/mlabonne/llm-course
#AI #MachineLearning #LLM #DataScience #Tech #GitHub
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⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
1. Hands-On AI Engineering 🛠️
A collection of AI applications and agent systems with practical use cases of LLM.
👉 https://github.com/Sumanth077/Hands-On-AI-Engineering
2. Hands-On Large Language Models 📘
Full code from the book Hands-On Large Language Models: from basics to fine-tuning.
👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
3. AI Agents for Beginners 🎓
A free course from Microsoft with 11 lessons on creating AI agents.
👉 https://github.com/microsoft/ai-agents-for-beginners
4. GenAI Agents 🤖
A large collection of tutorials and implementations of agent systems.
👉 https://github.com/NirDiamant/GenAI_Agents
5. Made With ML 🚀
About the development, deployment, and support of production-ready ML systems.
👉 https://github.com/GokuMohandas/Made-With-ML
6. Learn Harness Engineering ⚙️
A practical course on Harness Engineering for AI agents.
👉 https://github.com/walkinglabs/learn-harness-engineering
7. AutoResearch 🔬
Autonomous cycles of ML experiments from Andrej Karpathy.
👉 https://github.com/karpathy/autoresearch
8. Designing Machine Learning Systems 📚
Notes and materials from Chip Huyen's book.
👉 https://github.com/chiphuyen/dmls-book
9. Awesome LLM Inference ⚡
A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.
👉 https://github.com/xlite-dev/Awesome-LLM-Inference
10. LLM Course 🗺️
A practical course on LLM with a roadmap and Colab notebooks.
👉 https://github.com/mlabonne/llm-course
#AI #MachineLearning #LLM #DataScience #Tech #GitHub
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
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The Attention Mechanism allows transformer neural networks to determine the connection between words in a text and dynamically focus on the most important context. We will step by step implement the basic algorithm Scaled Dot-Product Attention, using classic matrices of queries (Query), keys (Key) and values (Value). This will help us to visually see how the attention weights are mathematically calculated and how the model matches the tokens with each other. 🧠✨
To start, we will install the PyTorch library for performing tensor calculations. 🛠️
pip install torch
The library has been successfully loaded and is ready for mathematical modeling of transformer layers. ✅
We will generate random vectors Query, Key and Value to simulate the passage of tokens through linear projections. 🎲
import torch
import torch.nn.functional as F
q = torch.randn(1, 3, 4) # (batch, seq_len, dim)
k = torch.randn(1, 3, 4)
v = torch.randn(1, 3, 4)
The tensors have been initialized and represent three hidden states for a sequence of three words. 📝
We will calculate the token similarity matrix through the scalar product and then scale it by the square root of the vector dimensions. 🔢
scores = torch.bmm(q, k.transpose(1, 2)) / (q.shape[-1] ** 0.5)
attention_weights = F.softmax(scores, dim=-1)
output = torch.bmm(attention_weights, v)
The scalar product has been translated into probability weights, based on which the final contextual vector has been formed. 🔄
A control run of the output dimension calculation:
python3 -c "import torch; q, k = torch.randn(1, 3, 4), torch.randn(1, 3, 4); print('Attention OK') if torch.bmm(q, k.transpose(1, 2)).shape == (1, 3, 3) else print('Error')"Expected output: Attention OK ✅
The Self-Attention formula lies at the heart of all modern LLMs, allowing them to process long contexts in parallel, unlike old recurrent networks (RNNs). Understanding this base is critically important for working with transformers, optimizing architectures and configuring KV-cache mechanisms. 🚀🧠
#PyTorch #Transformer #DeepLearning #AI #MachineLearning #LLM
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
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AI PYTHON 🌟
You’ve been invited to add the folder “AI PYTHON 🌟”, which includes 15 chats.
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