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Published on 6/2/2025
IAR Python Groq Llama-33-70 Qwen-qwq-32b

Python focused custom assistant. Thanks groq.

Rules
Prompts
Models
Context
groq Groq — Qwen-qwq-32b (Autocomplete-Testing) model icon

Groq — Qwen-qwq-32b (Autocomplete-Testing)

groq

4kinput·2.048koutput
openai Llama 3.3 70b Groq model icon

Llama 3.3 70b Groq

OpenAI

16kinput·6koutput
You are a Python coding assistant. You should always try to - Use type hints consistently - Write concise docstrings on functions and classes - Follow the PEP8 style guide
# Role
You are an expert in code assistance, giving concise, clear, and effective solutions, very briefly commented, and not more, unless more details are required.
## What you should be
Suggest your best fit solution (1 is enough, max. 2 if needed).
Try to avoid changes on any other code parts than that of the immediate problem vicinity.
Stick to the required task, do not make suggestions to the user about editing the code directly, or shifting to agent mode, or any other.
To stay within token limits, as per the former rules, also avoid intros and farewells.
Pythonhttps://docs.python.org/3/
PyTorchhttps://pytorch.org/docs/stable/index.html
Pandashttps://pandas.pydata.org/docs/
NumPyhttps://numpy.org/doc/stable/
Gradle Documentationhttps://docs.gradle.org/current/userguide/
Continuehttps://docs.continue.dev

Prompts

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Exploratory Data Analysis
Initial data exploration and key insights
Create an exploratory data analysis workflow that includes:

Data Overview:
- Basic statistics (mean, median, std, quartiles)
- Missing values and data types
- Unique value distributions

Visualizations:
- Numerical: histograms, box plots
- Categorical: bar charts, frequency plots
- Relationships: correlation matrices
- Temporal patterns (if applicable)

Quality Assessment:
- Outlier detection
- Data inconsistencies
- Value range validation

Insights & Documentation:
- Key findings summary
- Data quality issues
- Variable relationships
- Next steps recommendations
- Reproducible Jupyter notebook

The user has provided the following information:
RAG Pipeline Design
Comprehensive retrieval-augmented generation system design
Design a RAG (Retrieval-Augmented Generation) system with:

Document Processing:
- Text extraction strategy
- Chunking approach with size and overlap parameters
- Metadata extraction and enrichment
- Document hierarchy preservation

Vector Store Integration:
- Embedding model selection and rationale
- Vector database architecture
- Indexing strategy
- Query optimization

Retrieval Strategy:
- Hybrid search (vector + keyword)
- Re-ranking methodology
- Metadata filtering capabilities
- Multi-query reformulation

LLM Integration:
- Context window optimization
- Prompt engineering for retrieval
- Citation and source tracking
- Hallucination mitigation strategies

Evaluation Framework:
- Retrieval relevance metrics
- Answer accuracy measures
- Ground truth comparison
- End-to-end benchmarking

Deployment Architecture:
- Caching strategies
- Scaling considerations
- Latency optimization
- Monitoring approach

The user's knowledge base has the following characteristics:

Context

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@code
Reference specific functions or classes from throughout your project
@docs
Reference the contents from any documentation site
@diff
Reference all of the changes you've made to your current branch
@terminal
Reference the last command you ran in your IDE's terminal and its output
@problems
Get Problems from the current file
@folder
Uses the same retrieval mechanism as @Codebase, but only on a single folder
@codebase
Reference the most relevant snippets from your codebase
@currentFile
Reference the currently open file
@open
Reference the contents of all of your open files
@clipboard
Reference recent clipboard items

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