Top RAG Pain Points and Solutions

Bijit Ghosh
8 min readFeb 2, 2024

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Retrieval-augmented generation (RAG) models have emerged as a promising approach to improving the accuracy and relevance of generated text by leveraging external knowledge stored in documents. By retrieving and conditioning on relevant context documents, RAG models can produce more factual, in-depth, and specific responses compared to traditional language models.

However, as with any new technique, RAG models come with their own set of challenges that need to be addressed for them to reach their full potential. In this blog post, I’ll dive into the top pain points with RAG models and explore potential solutions to these issues. Specifically, focusing on the core difficulties that arise in areas like retrieval, conditioning, faithfulness, and safety.

For each major pain point, we first explain the background and describe why it poses a problem. We then suggest concrete methods, architectural changes, and innovations that can help mitigate or solve that challenge. Given the integral role played by the underlying language models in RAG models, we also discuss how advances in language model techniques can facilitate progress.

By the end, you will have a detailed understanding of the most pressing issues with RAG today and how the field is evolving to conquer them. Equipped with this knowledge, you will be able to build more advanced and responsible RAG models. Let’s get started!

Pain Point 1: Low-Quality Retrievals

Background: RAG models are heavily dependent on the quality of the retrieved context documents. If the retriever fails to find relevant, factual passages, it severely hampers the model’s ability to condition on useful information and produce accurate, in-depth responses. Unfortunately, off-the-shelf sparse vector retrievers often struggle with semantic matching and retrieving quality documents.

Solutions:

  • Fine-tune retrievers via supervised training signals or model feedback. This enhances relevance for the target domain.
  • Employ dense retriever models using models like DPR or ANCE for higher recall and relevance.
  • Experiment with multi-vector representations, approximate nearest neighbor search, and maximum inner product search to boost speed without hurting accuracy.
  • Cascade dense and sparse retrievers to combine strengths like relevance and speed.
  • For factuality, bias retrieval towards authoritative, trustworthy sources using credibility indicators.

LLM Role: Representation learning techniques from large language models can significantly improve semantic matching and relevance judgments for retriever models.

Pain Point 2: Lack of Coverage

Background: While external knowledge is indispensable for high-quality RAG outputs, even the largest corpora offer incomplete coverage of entities and concepts that users may query. Without access to comprehensive knowledge sources, the model returns uninformed, generic responses on niche or emerging topics.

Solutions:

  • Expand corpora by aggregating documents from diverse sources to increase likelihood of coverage.
  • Implement systems to detect coverage gaps during runtime and fall back to traditional language model completions.
  • Design modular architectures to add/update knowledge sources without full re-training.
  • Explore automated knowledge generation from language models to fill uncovered niches.

LLM Role: Pre-trained language models provide broad world knowledge that can temporarily cover gaps, which RAG models lack. Their ability to generate synthetic text could also help address coverage shortcomings.

Pain Point 3: Difficulty Conditioning on Context

Background: Even with good retrievals, RAG models often struggle to properly condition on context documents and incorporate external knowledge into generated text. Without effective context conditioning, they fail to produce specific, factual responses.

Solutions:

  • Strengthen contextualization via dedicated cross-attention transformer layers.
  • Pre-train language model decoders with self-supervised objectives that teach integrating external text.
  • Design training schemes that provide explicit conditioning signals and supervision.
  • Architect model parallel training for longer documents and enhanced memory.
  • Implement better handling of entity references to improve grounding.

LLM Role: Self-supervised pre-training of large language models equips them with skills like summarization that facilitate contextualization.

Pain Point 4: Hallucination and Fabrication

Background: Due to over-reliance on language model priors, RAG models frequently generate plausible but totally incorrect or unfaithful statements without verification in retrieved context. This hallucination misleads users.

Solutions:

  • Directly minimize likelihood of hallucinated text via training signals.
  • Programmatically analyze outputs to automatically detect fabrication based on mismatches with context.
  • Design a verification head network to explicitly validate statements before generation.
  • Use credibility indicators on retrieved documents to prevent conditioning on unreliable sources.
  • Weaken unconditional language model priors by focusing optimization on context grounding.

LLM Role: Large language models provide the strong priors that increase hallucination risks. But their scalability enables innovations like classifier-based hallucination detection.

Pain Point 5: Lack of Explanation and Interpretability

Background: Unlike traditional QA systems, RAG models offer no visibility into the reasoning behind their generated text. The explanations for their responses remain implicit and opaque rather than explicit. This harms debugability, trust, and responsible development.

Solutions:

  • Design model architectures to explicitly track evidence and explanations as structured chains/graphs.
  • Implement auxiliary heads to predict explanatory evidence like salient snippets.
  • Attach meaningful context tags at each generation step to track origin.
  • Generate natural language explanations that describe reasoning by citing sources.
  • Summarize key semantic connections between query and context that justify responses.

LLM Role: Large language models provide strong few-shot abilities that we can harness to generate post-hoc explanations of model reasoning with minimal additional training.

Pain Point 6: Safety and Control Risks

Background: By conditioning text generation on arbitrary web documents, RAG models can propagate harmful, biased, or toxic content in their outputs. Their open-ended generation also increases risks of malicious use and lacks controls.

Solutions:

  • Thoroughly vet documents and implement safety classifiers during corpus creation.
  • Develop run-time filters using classifier frameworks like OPT or GPT-3’s Classifier to catch unsafe outputs.
  • Design control schemes via input prompting, output re-writing, and fine-tuning approaches.
  • Restrict generation freedom by steering towards retrieval contexts and focusing on grounded outputs.
  • Implement modular content controls using classifier APIs and external safety services.

LLM Role: Large language models provide established techniques like classifier fine-tuning that enable safeguards while maintaining generation quality.

Pain Point 7: Slow Inference Speed

Background: The coupling of retrieval with generation hinders RAG models from matching the latency of standard language models. The inference pipeline lacks optimization for real-time applications that demand millisecond response.

Solutions:

  • Optimize tokenization, encoding, and retrieval inference to minimize overhead before generation.
  • Employ efficient approximate nearest neighbor indexes using libraries like NMSLIB, FAISS, or ScaNN.
  • Take advantage of model parallelism and batch retrievals+generations for pipeline efficiency.
  • Design model distillation methods to compress retriever-generator combo with minimal quality loss.
  • Shift retrievals offline wherever possible to avoid run-time bottlenecks.

LLM Role: Lightweight, optimized decoders from large language models complement retriever speeds for faster end-to-end latency.

Pain Point 8: Difficult Personalization and Grounding

Background: RAG models trained on generic corpora lack capacities to produce responses tailored to specific user needs, contexts, and queries. They cannot resolve ambiguous information requests without personal understanding.

Solutions:

  • Design persona context memories to track user profiles and context across conversations.
  • Fine-tune RAG models on labeled query->response pairs matching target users.
  • Implement multi-task training to ground responses on prior dialogue and user feedback.
  • Develop few-shot personalization techniques leveraging meta learning.
  • Architect user-specific expansion modules to complement retrieval corpora.

LLM Role: Large language model abilities in few-shot learning and remembering context enable rapid fine-tuning for personalization with limited data from new users.

Pain Point 9: Difficulty Evaluating Quality

Background: The diversity of possible grounded responses makes it challenging to reliably evaluate the correctness and quality of RAG model outputs using automated metrics. Human evaluation also lacks scalability. This stifles iterative improvement.

Solutions:

  • Generate annotated test sets with expert rationales to enable standardized evaluation.
  • Develop specialized metrics based on semantics rather than n-gram overlap.
  • Quantify key axes like relevance, coherence, consistency separately with targeted automatic evaluations.
  • Design online learning schemes leveraging user feedback signals as personalized quality judgments.
  • Build interactive evaluation interfaces centered around annotations rather than numeric scores.

LLM Role: Few and zero-shot abilities allow leveraging language model rankings and existing test sets as preliminary quality benchmarks before costlier human review.

Pain Point 10: Difficulty Maintaining Truthfulness

Background: Without explicit mechanisms to verify facts, RAG models rely on spurious patterns from pre-training and inaccurate retrieval contexts, generating plausible-sounding but false claims. This harms trustworthiness.

Solutions:

  • Develop auxiliary heads to predict veracity directly from retrieved contexts.
  • Enable interactive identification of faulty claims to improve via online learning.
  • Incorporate structural knowledge bases to fact check responses against known entities and relations.
  • Design confidence estimation approaches to quantify certainty and flag unverified statements.
  • Implement provenance tracking to responsibility attribute statements to sources.
  • Minimize open-ended generation in favor of extractive summarization from verified contexts.

While LLMs enable significant progress in areas like commonsense reasoning and fact verification, their scope remains limited. As such, pairing retrieval with structured knowledge bases and human-in-the-loop interaction remains vital for ensuring truthfulness in open-domain question answering scenarios. In addition to the challenges discussed so far, RAG models face critical security vulnerabilities that malicious actors could exploit if left unaddressed.

Pain Point 11 — Poisoning Attacks

Background: Adversaries can manipulate retrieved documents and contexts to inject harmful behaviors in conditioned generations. Since RAG models implicitly trust retrievals, such poisoning attacks easily undermine model integrity.

Solutions:

  • Perform rigorous audits of knowledge sources and document provenance during corpus creation.
  • Detoxify documents with safety classifiers before ingestion into corpora.
  • Detect poisoning attempts via outlier and XAB detection on retrieved contexts.
  • Diversify knowledge sources to limit reliance on potentially compromised ones.

Pain Point 12 — Model Inversion

Background: By analyzing RAG model outputs, attackers could partially reconstruct sensitive texts from training corpora and retrieval stores, violating expectations of privacy and confidentiality.

Solutions:

  • Formal privacy analysis to guide aggregation, filtering, and splitting of corpora.
  • Differential privacy techniques by adding noise during training.
  • Restrict generation from sensitive retrieved contexts to minimize exposures.

Pain Point 13 — Backdoor Triggers

Background: Complex RAG model pipelines enable new attack surfaces for planting backdoors via tainted contexts that get implicitly encoded by generators.

Solutions:

  • Perform rigorous testing with trap contexts containing pseudo-triggers to catch vulnerabilities.
  • Employ universal adversarial trigger detection techniques to identify anomalies.
  • Continuously monitor model behavior on benign sets to detect deviation.

By acknowledging and safeguarding against emerging security threats, we can nurture trust in RAG technology despite adversaries.

The Path Forward

In this extensive analysis, we explored the top pain points plaguing state-of-the-art RAG models, spanning challenges with retrieval quality, safety, speed, evaluation difficulty, and more. But for each issue, i also outlined promising solutions leveraging methods like improved training objectives, model architectures, data augmentation techniques, and optimized inference pipelines.

Furthermore, we discussed how the exciting advances happening with LLMs can provide building blocks to help confront many of these challenges. Unsupervised pre-training paradigms continue to enhance capacities like semantic search, few-shot learning, summarization, and consistency tracking that alleviate shortcomings with existing RAG designs.

However, fully addressing these multifaceted issues requires going beyond improving language models alone. It necessitates cross-pollination with complementary fields like information retrieval, knowledge representation, human-computer interaction, and machine learning to create next-generation RAG models.

The interdisciplinary nature of RAG research makes it uniquely positioned to drive major progress in conversational AI — marrying retrieval, reasoning, and language understanding. As researchers consolidate solutions to the biggest blockers identified here, we inch closer to tapping into the promise of this technology.

While considerable work remains to mitigate risks and enhance reliability before real-world RAG adoption, this blog post outlined actionable research directions that give us reasons for optimism. We hope the analysis empowers more impactful explorations into robust and beneficial text generation augmented by access to human knowledge.

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Bijit Ghosh

CTO | Senior Engineering Leader focused on Cloud Native | AI/ML | DevSecOps