Row 7734
Content Data
This page contains data entry 7734 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.
The following guide looks forward to what new developments we anticipate will come for AI programming in the next year - how flow engineering paradigm could provide shift to LLM pipelines that allow data processing steps, external data pulls, and intermediate model calls to all work together to further AI reasoning: [From Prompt Engineering to Flow Engineering: 6 More AI Breakthroughs to Expect](https://www.codium.ai/blog/from-prompt-engineering-to-flow-engineering-6-more-ai-breakthroughs-to-expect-in-2024/)
* LLM information grounding and referencing * Efficiently connecting LLMs to tools * Larger context sizes * LLM ecosystem maturity leading to cost reductions * Improving fine-tuning * AI Alignment
| Field | Value |
|---|---|
| text | The following guide looks forward to what new developments we anticipate will come for AI programming in the next year - how flow engineering paradigm could provide shift to LLM pipelines that allow data processing steps, external data pulls, and intermediate model calls to all work together to further AI reasoning: [From Prompt Engineering to Flow Engineering: 6 More AI Breakthroughs to Expect](https://www.codium.ai/blog/from-prompt-engineering-to-flow-engineering-6-more-ai-breakthroughs-to-ex… |
| label | r/gpt3 |
| dataType | post |
| communityName | r/GPT3 |
| datetime | 2024-05-17 |
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Raw Record
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"text": "The following guide looks forward to what new developments we anticipate will come for AI programming in the next year - \nhow flow engineering paradigm could provide shift to LLM pipelines that allow data processing steps, external data pulls, and intermediate model calls to all work together to further AI reasoning: [From Prompt Engineering to Flow Engineering: 6 More AI Breakthroughs to Expect](https://www.codium.ai/blog/from-prompt-engineering-to-flow-engineering-6-more-ai-breakthroughs-to-expect-in-2024/)\n\n* LLM information grounding and referencing\n* Efficiently connecting LLMs to tools\n* Larger context sizes\n* LLM ecosystem maturity leading to cost reductions\n* Improving fine-tuning\n* AI Alignment",
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}
Entry Information
- Entry ID: 7734
- Repository: Axioma AXP
- Dataset: arrmlet/reddit_dataset_36
- Total Entries: 100,000