Unlocking the Power of Large Language Models (LLMs) for Business Applications
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Muhammad Imran Zaman PRO
ImranzamanML
AI & ML interests
Results-driven Machine Learning Engineer with 7+ years of experience leading teams and delivering advanced AI solutions that increased revenue by up to 40%. Proven track record in enhancing business performance through consultancy and expertise in NLP, Computer Vision, LLM models and end-to-end ML pipelines. Skilled in managing critical situations and collaborating with cross-functional teams to implement scalable, impactful solutions. Kaggle Grandmaster and top performer in global competitions, dedicated to staying at the forefront of AI advancements.
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Easy steps for an effective RAG pipeline with LLM models!
1. Document Embedding & Indexing
We can start with the use of embedding models to vectorize documents, store them in vector databases (Elasticsearch, Pinecone, Weaviate) for efficient retrieval.
2. Smart Querying
Then we can generate query embeddings, retrieve top-K relevant chunks and can apply hybrid search if needed for better precision.
3. Context Management
We can concatenate retrieved chunks, optimize chunk order and keep within token limits to preserve response coherence.
4. Prompt Engineering
Then we can instruct the LLM to leverage retrieved context, using clear instructions to prioritize the provided information.
5. Post-Processing
Finally we can implement response verification, fact-checking and integrate feedback loops to refine the responses.
Happy to connect :)
1. Document Embedding & Indexing
We can start with the use of embedding models to vectorize documents, store them in vector databases (Elasticsearch, Pinecone, Weaviate) for efficient retrieval.
2. Smart Querying
Then we can generate query embeddings, retrieve top-K relevant chunks and can apply hybrid search if needed for better precision.
3. Context Management
We can concatenate retrieved chunks, optimize chunk order and keep within token limits to preserve response coherence.
4. Prompt Engineering
Then we can instruct the LLM to leverage retrieved context, using clear instructions to prioritize the provided information.
5. Post-Processing
Finally we can implement response verification, fact-checking and integrate feedback loops to refine the responses.
Happy to connect :)
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1653
Are you a Professional Python Developer? Here is why Logging is important for debugging, tracking and monitoring the code
Logging
Logging is very important part of any project you start. It help you to track the execution of a program, debug issues, monitor system performance and keep an audit trail of events.
Basic Logging Setup
The basic way to add logging to a Python code is by using the logging.basicConfig() function. This function set up basic configuration for logging messages to either console or to a file.
Here is how we can use basic console logging
Logging to a File
We can also save the log to a file instead of console. For this, we can add the filename parameter to logging.basicConfig().
You can read more on my medium blog https://medium.com/@imranzaman-5202/are-you-a-professional-python-developer-8596e2b2edaa
Logging
Logging is very important part of any project you start. It help you to track the execution of a program, debug issues, monitor system performance and keep an audit trail of events.
Basic Logging Setup
The basic way to add logging to a Python code is by using the logging.basicConfig() function. This function set up basic configuration for logging messages to either console or to a file.
Here is how we can use basic console logging
#Call built in library
import logging
# lets call library and start logging
logging.basicConfig(level=logging.DEBUG) #you can add more format specifier
# It will show on the console since we did not added filename to save logs
logging.debug('Here we go for debug message')
logging.info('Here we go for info message')
logging.warning('Here we go for warning message')
logging.error('Here we go for error message')
logging.critical('Here we go for critical message')
#Note:
# If you want to add anything in the log then do like this way
records=100
logging.debug('There are total %s number of records.', records)
# same like string format
lost=20
logging.debug('There are total %s number of records from which %s are lost', records, lost)Logging to a File
We can also save the log to a file instead of console. For this, we can add the filename parameter to logging.basicConfig().
import logging
# Saving the log to a file. The logs will be written to app.log
logging.basicConfig(filename='app.log', level=logging.DEBUG)
logging.debug('Here we go for debug message')
logging.info('Here we go for info message')
logging.warning('Here we go for warning message')
logging.error('Here we go for error message')
logging.critical('Here we go for critical message')You can read more on my medium blog https://medium.com/@imranzaman-5202/are-you-a-professional-python-developer-8596e2b2edaa
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models
8
ImranzamanML/arabert_finetuned_model
Feature Extraction
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Updated
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ImranzamanML/arabic_finetuned_model
Feature Extraction
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Updated
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ImranzamanML/multilingual-mpnet-finetuned
Sentence Similarity
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ImranzamanML/1B_finetuned_llama3.2
Updated
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ImranzamanML/7B_finetuned_Mistral
Updated
ImranzamanML/finetuned_llama3.1
Updated
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ImranzamanML/ai_psychiatrist
Updated
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ImranzamanML/GEFS-language-detector
Text Classification
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