🤖 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

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🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨

We found this interactive website that shows you visually how transformer models work. 🌐📊

Transformer Explainer:
https://poloclub.github.io/transformer-explainer/

#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech

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Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚

This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊

It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖

Free public repository on GitHub. 💻✨

https://github.com/dair-ai/Mathematics-for-ML

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🔖 A huge open-source course on AI Engineering from scratch

In the repository, we've collected:
— 435 lessons;
— 320+ hours of content;
— Python, TypeScript, and Rust;
— AI agents, MCP servers, prompts, and AI skills.

Moreover, almost every lesson includes practical tasks, so this isn't just theory, but a full-fledged roadmap for AI Engineering. 🚀

⛓️ Link to the repository
https://github.com/rohitg00/ai-engineering-from-scratch

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Transformer implementations for vision, audio, and AI agents 🤖👁️🎵

Repo: https://github.com/Nicolepcx/transformers-the-definitive-guide

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Introduction to Deep RL and DQN

Link: https://www.dailydoseofds.com/rl-course-part-6/

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Optimizing the model's performance through Prompt Tuning with the PEFT library.

✨ Full-fledged fine-tuning of language models requires a huge amount of video memory and completely overwrites the network's weights. We will apply the Prompt Tuning method (retraining virtual token prompts), which freezes the main model and adjusts only a tiny matrix of virtual embeddings. This allows adapting AI to a narrow task using a regular user's graphics card and without the risk of destroying the neural network's basic knowledge.

📦 First, we will install the necessary libraries for working with transformers and effective fine-tuning methods (PEFT).

pip install torch transformers peft

✅ The packages have been successfully installed in the system and are ready for configuring lightweight training. We will create a basic Prompt Tuning configuration for training just twenty virtual tokens instead of billions of model parameters.

from peft import PromptTuningConfig, PromptTuningInit, get_peft_model
from transformers import AutoModelForCausalLM

peft_config = PromptTuningConfig(
    task_type="CAUSAL_LM",
    prompt_tuning_init=PromptTuningInit.TEXT,
    num_virtual_tokens=20,
    prompt_tuning_init_text="Classify the sentiment of this text:",
    tokenizer_name_or_path="gpt2"
)

🔄 The configuration is initialized and links the text prompt to the trainable virtual embeddings. We will wrap the base model in a PEFT container to freeze the main weights and leave only the new tokens available for gradient descent.

base_model = AutoModelForCausalLM.from_pretrained("gpt2")
peft_model = get_peft_model(base_model, peft_config)
peft_model.print_trainable_parameters()

🚀 The model is ready for training, and the percentage of active parameters will be displayed on the screen (usually less than 0.01%).

python3 -c "from peft import PromptTuningConfig; print('PEFT Setup: OK')"

📝 Expected output: PEFT Setup: OK

pip uninstall peft -y

💡 Prompt Tuning — an ideal choice when you need to train a model for many different customers or tasks simultaneously. Instead of gigabyte-sized copies of neural networks, you store only lightweight configuration files weighing a few kilobytes, dynamically substituting them at inference.

#PromptTuning #PEFT #AI #MachineLearning #DeepLearning #DataScience

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