Stay Focused in Long Analyses
Keep analysis sessions short and focused to avoid context errors in long conversations.
Long conversations can introduce errors because the AI must track many previous messages, outputs, and variables.
When the context becomes large, the AI may:
- Confuse file paths or variable names
- Mix results from different analysis steps
- Generate plausible but incorrect statistics
- Lose earlier outputs from active memory
The safest workflow is to keep each analysis session short and focused.
Core strategy: one analysis = one session
Instead of performing an entire workflow in one long conversation, split it into focused sessions:
| Avoid: one long session doing | Prefer: separate focused sessions |
|---|---|
| QC → Clustering → | Session 1: QC and Preprocessing |
| DE → Enrichment → | Session 2: Clustering |
| Visualization | Session 3: Differential Expression |
| Session 4: Pathway Enrichment |
Each session stays short, focused, and context-clean.
Practical tips
1. One session per analysis step
Start a new session for each major stage of the analysis.
Example workflow:
- Session 1: QC and preprocessing → save
adata_qc.h5ad - Session 2: load
adata_qc.h5ad→ clustering and annotation → saveadata_annotated.h5ad
The second session starts with a clean context.
2. Save checkpoints after major steps
Always save intermediate results.
# After QC
adata.write_h5ad("/home/user/user_data/Project/checkpoints/adata_qc.h5ad")
# After clustering
adata.write_h5ad("/home/user/user_data/Project/checkpoints/adata_clustered.h5ad")
# After annotation
adata.write_h5ad("/home/user/user_data/Project/checkpoints/adata_annotated.h5ad")3. Start new sessions with a context summary
When continuing work in a new session, begin with a short summary.
Continuing a scRNA-seq analysis.
Previous session saved the annotated AnnData at:
/home/user/user_data/Project/adata_annotated.h5ad
Cell types assigned:
T cells, B cells, Macrophages, NK cells
Clustering resolution:
0.5 using the Leiden algorithm
Goal of this session:
Run differential expression between Treated and Control within each cell typeThis provides accurate context instead of relying on the AI’s memory.
4. Focus on one task per prompt
Avoid combining multiple steps in one request.
Instead of: “Run DE, do pathway enrichment, create a heatmap, annotate top genes, and save everything.”
Break it into steps: Run differential expression → Perform pathway enrichment → Create visualizations.
5. Treat the notebook output as the ground truth
Always trust the actual notebook output, not the AI’s summary in chat.
6. Reset sessions when conversations become long
If a conversation exceeds ~15–20 exchanges, summarize the key findings and start a new session.
Message limit per conversation
Your plan sets the maximum number of messages in one conversation. From November 2, 2026, the limit is 150 messages on Free and Individual and 750 on Lab Basic. When a conversation reaches it, Drylab shows This conversation has reached its limit instead of sending your message. Click Start new conversation: the message you typed is carried over to the new chat, so nothing is lost. Start the new chat with a short context summary, as in tip 3.