prompt engineering

topic18 events

papersTODAY 04:00 UTC

Paper Argues Controlled Semantics Improve LLM Knowledge Graph Query Execution

A new arXiv paper examines how large language models receive data model concepts when translating natural language into database queries. The authors argue that conveying these concepts through controlled semantics rather than informal prompt prose, schema dumps, and examples leads to better query execution. The work focuses on natural language knowledge graph querying within the LLM context window.

knowledge-graphs/llm/query-execution/research

papersTODAY 04:00 UTC

Paper Proposes Exploration-Guided Prompt Scaffolding for Multimodal RL Post-Training

A new arXiv paper argues that training prompts in online reinforcement learning vary widely in how useful they are to the current policy, with some already solved and others too hard to give a dependable learning signal. The authors propose an exploration-guided prompt scaffolding method that selects or structures prompts for multimodal reinforcement post-training so rollouts are better spent. The work appears in both the cs.AI and cs.LG listings as arXiv:2609.15051v1.

reinforcement-learning/multimodal/training-data/arxiv

papersTODAY 04:00 UTC

Study examines how users iterate prompts to explore narrative space in LLM story generation

A new arXiv paper analyzes public chatbot logs to understand how people write stories with large language models. The authors find that users repeatedly revise their prompts, tweaking characters and other story elements to explore different narrative directions. The work frames this behavior as navigation through a space of possible stories.

story-generation/prompting/user-behavior/llm

papersTODAY 04:00 UTC

CoTAL: Human-in-the-Loop Prompt Engineering for Formative Assessment Scoring

Researchers present CoTAL, a human-in-the-loop prompt engineering method for using large language models to score formative assessments and generate feedback for students. The work examines how well such prompting approaches generalize across educational contexts, with teachers involved in refining the prompts. It is published as an arXiv preprint in the computation and language category.

education/prompt-engineering/llm/human-in-the-loop

papersTODAY 04:00 UTC

CALICO System Aligns LLM Annotation Prompts With Expert Codebooks

Researchers present CALICO, a human-centered system that helps domain experts turn their annotation codebooks into prompts for large language models. The work targets gaps in existing pipelines, which offer little support for producing prompts that stay reliable, easy to revise, and auditable. It is described in a paper posted to arXiv under the cs.CL category.

annotation/llm/research/human-in-the-loop

papersTODAY 04:00 UTC

Data-Efficient Sample Selection for In-Context Learning

A new arXiv paper tackles the problem of choosing which demonstration examples to include in a prompt when using in-context learning with large language models. Because the space of possible example subsets is combinatorially large, the authors propose a data-efficient approach to selecting good combinations without exhaustive search. The work aims to improve how LLMs adapt to new tasks without fine-tuning.

in-context-learning/llm/research/sample-selection

tipsYESTERDAY 15:47 UTC

AWS guide outlines decision framework for choosing generative AI customization methods

AWS published an eight-step decision framework to help teams choose how much to customize generative AI models. The guidance spans prompt engineering, retrieval-augmented generation, fine-tuning, continued pre-training, and Amazon Nova Forge, recommending that teams start with simpler methods and escalate only when needed.

aws/fine-tuning/rag/prompt-engineering

tipsSEP 12 12:40 UTC

OpenAI advises leaner prompts and fewer guardrails for GPT-6 Astra

OpenAI developer Eric Provencher warns that verbose skill descriptions, broad mandatory-reading requirements, and inflexible approval gates can slow down GPT-6 Astra when used in Codex. He recommends that developers trim instructions to fit the specific task and clearly state what a completed job looks like, since stronger models require less detailed guidance.

prompting/openai/codex/gpt-6-astra

papersSEP 12 04:00 UTC

Soft Prompting Approach Adapts Vision-Language Models with Few Shots

A new arXiv paper tackles few-shot object detection with vision-language models in domains that differ from their training data, such as aerial, industrial, and medical imagery, where only ten labeled images are available. Rather than optimizing discrete text prompts, the authors propose a soft prompting method for adaptation. The work is a cross-listing on arXiv cs.AI.

vision-language/few-shot/prompting/object-detection

papersSEP 12 04:00 UTC

AcFlow Method Adds Style and Concept Control to Text-to-Image Diffusion Transformers

A new arXiv paper introduces AcFlow, a technique that controls text-to-image diffusion transformers by learning conditional activation flow at inference time. The approach aims to give users adjustable control over style intensity and to suppress unwanted concepts that direct prompting handles poorly. The work targets the limits of prompt-based interfaces for generative image models.

diffusion-models/text-to-image/research/controllability

papersSEP 12 04:00 UTC

KuaiRP Role-Playing Model Series Detailed in New arXiv Technical Report

A newly posted arXiv paper lays out the full technical approach behind the KuaiRP family of role-playing models. The authors say the design targets four goals, including lighter prompt engineering, more consistent output quality, and built-in domain capabilities. Further implementation and evaluation details were not included in the available excerpt of the report.

role-playing/arxiv/language-models/research

tipsSEP 10 06:00 UTC

t3n Guide Explains Deep Research and Offers a Starter Prompt Template

German tech outlet t3n has published a beginner-oriented overview of Deep Research, the practice of running extensive automated research with AI tools. The episode of its MeisterPrompter series includes a prompt template intended to help newcomers get started. It is aimed at readers looking for a practical entry point rather than product announcements.

prompting/deep-research/tutorial/germany

papersSEP 10 04:00 UTC

Paper proposes measuring and optimizing LLM agent harnesses without retraining models

A new arXiv paper studies how LLM tool agents can be improved by modifying the runtime harness around a fixed model, including prompts, tool interfaces, middleware, state handling, and recovery logic. The authors frame this as a resource-bounded harness selection problem, arguing that agent performance can be improved without retraining. The work offers ways to measure and optimize these harness components systematically.

agents/evaluation/benchmarks

papersSEP 10 04:00 UTC

Answer paths and grounding instructions studied in LLM question answering over knowledge graphs

A graph retrieval-augmented generation pipeline must decide which knowledge graph triples to place in the prompt, what syntax to serialize them in, in what order, and with what instruction telling the model how to use them. This arXiv paper varies all four design choices across six large language models to assess how each factor affects question answering performance.

rag/knowledge-graphs/qa/llms

papersSEP 10 04:00 UTC

Zone of Proximal Policy Optimization: teacher guidance via prompts, not gradients

A new arXiv paper argues that knowledge distillation breaks down when the student model is much smaller than its teacher, because imitating the teacher's logits locks the student into its sharpest output modes and harms generalization. The authors propose letting the large teacher guide the small student through prompts during reinforcement-learning fine-tuning instead of through gradient-based distillation. The work appears in the computational linguistics category on arXiv.

distillation/reinforcement-learning/small-models/training

papersSEP 10 04:00 UTC

DexterSQL Paper Proposes Deep Schema Exploration and Rule-Based Correction for Text-to-SQL

A new arXiv paper, cross-listed in cs.CL and cs.AI, introduces DexterSQL, a prompting-based approach to text-to-SQL generation that avoids fine-tuning the underlying large language model. The method targets shortcomings of existing prompting techniques, such as relying on coarse-grained schema information, by exploring database schemas in greater depth and applying rule-based corrections to the generated queries.

text-to-sql/llm/databases/research

papersSEP 10 04:00 UTC

Test-time Prompt Refinement Method Reduces Prompt Sensitivity in Text-to-Image Models

A new preprint addresses how text-to-image generators can produce varying results when the wording of a prompt is only slightly changed. The authors present a closed-loop technique that adjusts the prompt during inference, with the goal of making generated images better match the intended meaning. The revised v2 of the arXiv paper describes the approach and its evaluation.

text-to-image/prompting/research/generative-ai

papersSEP 10 04:00 UTC

Self-play in code distills a text harness for black-box optimization, arXiv study finds

Researchers explore whether a language-model agent can acquire a numerical search strategy through executable practice and then transfer it as plain text. Targeting low-budget black-box optimization, where unaided LLMs fall short of strong classical optimizers, the approach uses self-play in code to automatically build the harness. The work suggests learned optimization behavior can be distilled into reusable text prompts instead of manually engineered ones.

agents/self-play/optimization/prompting